Qwen Integration with Cursor

how can I tie qwen with cursor

To integrate the Qwen LLM (by Alibaba) with the Cursor IDE, you'll need to ensure both compatibility and efficient communication between the LLM and the IDE. Here are the steps to set up this integration: ### 1. Set Up Qwen LLM - **Local Setup:** If you're running Qwen on a local server, ensure you have all dependencies installed and the server is accessible. Check Qwen's documentation for setup specifics, including any requirements like Docker or specific environment configurations. - **API Access:** If Qwen offers an API endpoint (either from a local or cloud server), note the endpoint URL and any necessary authentication tokens. This API will be used for requests from Cursor IDE. ### 2. Integrate with Cursor IDE Cursor doesn’t natively support all LLMs directly, so we’ll use an API-based approach to connect Qwen to Cursor: - **OpenAI API Emulation**: Cursor IDE is optimized to work with OpenAI’s API. If Qwen doesn’t support OpenAI's API format directly, you might need to write a wrapper around Qwen’s API to make it compatible with the OpenAI format (this often includes defining endpoints for `chat`, `completions`, etc.). - **Middleware Setup**: Create a small middleware server (using Flask, FastAPI, or Node.js) that translates OpenAI-style requests from Cursor into the format Qwen’s API expects, and then relays the responses back to Cursor. This middleware would: - Accept OpenAI-style requests (e.g., `/v1/completions` or `/v1/chat/completions`). - Convert the input to the expected format for Qwen. - Forward the request to the Qwen API. - Parse Qwen’s response and reformat it to the OpenAI format before sending it back to Cursor. ### 3. Configure Cursor IDE - **Custom API Configuration**: In Cursor, you might be able to point the IDE to your middleware server as if it were an OpenAI API. Enter the server address for your middleware (e.g., `http://localhost:5000/v1`) in the OpenAI API configuration settings. - **Testing**: Run a test prompt in Cursor to ensure that requests are processed correctly and that Qwen responds via the middleware. This setup allows you to use Qwen with Cursor while emulating the expected OpenAI API structure, ensuring a smooth integration despite Cursor’s default configurations.

I would like to use Qwen2.5-Coder-1.5B for real-time code completion (and fill in the middle). I would like to switch from GitHub Copilot to a local LLM, to not be dependent on commercial entities or internet access. Some questions: Which quants are recommended? Memory usage isn't the biggest issue since it's a small model, but long context (500-1000 lines of Python code) would be appreciated to have proper context. Which software could fulfil this

To use Qwen2.5-Coder-1.5B for real-time code completion with long context, here's how you can approach the setup: ### 1. **Recommended Quantization Methods** Quantization will help reduce memory usage and improve inference efficiency while maintaining reasonable performance. Since you're working with a smaller model (1.5B parameters), it's not as memory-hungry, but for long contexts, you can still optimize it: - **GPTQ (Generalized Quantization)**: This method is effective for large models, and it should also work well with Qwen2.5-Coder-1.5B. It strikes a good balance between model performance and memory efficiency. GPTQ can be applied to the model to reduce precision (e.g., from FP16 to INT4/INT8) without significant quality loss in inference, especially for code completion tasks. - **QLoRA (Quantized Low-Rank Adaptation)**: This is a more recent method that can also be considered for model tuning and efficient inference. It’s well-suited for models requiring adaptation with limited resources while preserving task-specific performance. - **LoRA (Low-Rank Adaptation)**: If memory is not a big issue, LoRA might be used to adapt the model to specialized tasks without significantly altering the underlying model architecture. Given your use case of code completion and long context, **GPTQ** might be the most straightforward and effective approach for you. ### 2. **Software for Efficient Inference** Here are the best options for running Qwen2.5-Coder-1.5B with long context, focusing on local, offline inference and real-time code completion: #### **For Long Context Handling (500-1000 lines of Python code)**: - **Haystack**: Haystack by deepset provides a flexible framework for working with long-context models. It supports various backends, including Hugging Face, and allows you to split long input into chunks and perform efficient inference. It can be customized to handle long context windows, making it ideal for your Python code completion requirements. - **FastAPI + Local Inference Server**: You could create a custom FastAPI server to handle real-time requests to Qwen2.5-Coder. This setup allows you to integrate long context handling via chunking or sliding windows and provide results based on code context. This would be more hands-on but gives you the most flexibility. #### **For Local LLM Hosting**: - **NVIDIA Triton Inference Server**: If you're running on a compatible GPU (NVIDIA), Triton provides high-performance inference support for large models and supports quantization techniques. It can handle long context and optimize the model for faster code completion. - **GPT-NeoX or Open-Chat**: Both are open-source frameworks designed to run large language models locally and support advanced features like long context windows. These tools can be adapted to your specific needs, like implementing a real-time code completion server. #### **Context Management**: - **Chroma or FAISS**: These are vector databases that can be used to store and retrieve relevant context from large codebases. For instance, when you input a 1000-line Python code chunk, you could split the code into smaller segments, store them in a vector database, and dynamically retrieve the most relevant context for each code completion request. ### 3. **Implementation Considerations** - **Batching and Chunking**: Since your context size might exceed the maximum token limit, consider batching or chunking the input into manageable pieces while maintaining code flow coherence. You could implement a system that preprocesses the code into sections, processes them in context windows, and intelligently merges them back for the final output. - **Interactive Interface**: Use a real-time IDE plugin or desktop application (like Visual Studio Code with a local extension) that communicates with your local Qwen2.5-Coder instance. This provides a smooth experience for code completion while leveraging long context for better performance. In summary, **GPTQ quantization** combined with **FastAPI** or **Haystack** for context management should fulfill your needs for efficient real-time Python code completion using Qwen2.5-Coder-1.5B, ensuring both long context handling and local operation.

explain how to do this: FastAPI + Local Inference Server: You could create a custom FastAPI server to handle real-time requests to Qwen2.5-Coder. This setup allows you to integrate long context handling via chunking or sliding windows and provide results based on code context. This would be more hands-on but gives you the most flexibility.

Setting up a custom FastAPI server to run Qwen2.5-Coder-1.5B locally for real-time code completion allows you to create a flexible, controlled environment for handling long contexts. Here’s how to set up this server and manage the context effectively. ### Step 1: Set Up Qwen2.5-Coder Locally First, make sure Qwen2.5-Coder-1.5B is set up and accessible on your system: 1. **Download and Configure Qwen2.5-Coder-1.5B**: If it’s hosted on Hugging Face, use their model hub or a Docker image (if available) to download and set up the model locally. 2. **Install Dependencies**: Ensure you have `transformers`, `torch`, and other dependencies required to run Qwen. You can install these with: ```bash pip install transformers torch fastapi uvicorn ``` 3. **Quantization (Optional)**: Apply GPTQ or another quantization method if needed to optimize the model’s memory and speed without sacrificing too much quality. ### Step 2: Create a FastAPI Server This server will act as an interface for making real-time inference requests to the Qwen2.5-Coder model. 1. **Initialize FastAPI App**: Create a new Python file, say `main.py`, and import FastAPI and the necessary libraries. 2. **Load the Model**: Load Qwen2.5-Coder into memory when the FastAPI app starts. This way, it’s ready to serve requests immediately. ```python from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() # Load model and tokenizer globally model_name = "Qwen/Qwen-2.5-Coder-1.5B" # replace with the local path if downloaded tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() # Set the device to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ``` ### Step 3: Define an API Endpoint for Code Completion Define a route that takes code as input, handles long contexts, and returns completions. 1. **Chunking/Sliding Windows**: To handle long contexts (e.g., 500-1000 lines), split the input into chunks of a size that fits within the model's maximum token limit. 2. **Processing the Request**: When a request is received, the server tokenizes the input, manages the context, and generates a response. ```python @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: # Tokenize input prompt inputs = tokenizer(prompt, return_tensors="pt").to(device) # Handle long context with sliding windows input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions # max token limit of the model if input_length > max_token_length: # Sliding window mechanism context_size = max_token_length - max_length # Leave space for completion prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: # If the input is within limit, use it as is inputs = inputs # Generate code completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # Adjust sampling temperature if desired ) # Decode and return the generated text completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) ``` ### Step 4: Run the Server Start the FastAPI server locally using Uvicorn: ```bash uvicorn main:app --host 0.0.0.0 --port 8000 ``` The server will be accessible at `http://localhost:8000`, and you can test the `/complete_code/` endpoint by sending a POST request with the code prompt. ### Step 5: Handling Real-Time Requests and Context Management To handle real-time completions, consider integrating the following strategies: - **Context Management**: For long input, use a sliding window to keep the most relevant part of the context. This ensures that only the necessary code lines are passed to the model, preserving memory and speed. - **Caching Responses**: To further optimize real-time performance, consider caching previous completions if you expect repeated requests with similar prompts. ### Step 6: Testing and Integrating with Your IDE You can test this setup by using an HTTP client like `curl` or Postman: ```bash curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 50}' ``` For IDE integration, you can create a plugin or extension in your code editor (e.g., VS Code) that sends code snippets to this API for real-time code completion. This setup gives you full control over Qwen’s code completion behavior and allows you to fine-tune it for your needs without relying on external, commercial solutions.

'''You are to write in the style of {persona.get('name', 'Unknown Author')}, a writer with the following characteristics: {build_characteristic_list(persona)} Psychological Traits: {build_psychological_traits(psychological_traits)} Additional background information: {build_background_info(persona)} { "name": "Anonymous Meta Employee", "vocabulary_complexity": 7, "sentence_structure": "complex", "paragraph_organization": "stream-of-consciousness", "idiom_usage": 2, "metaphor_frequency": 3, "simile_frequency": 1, "tone": "informal", "punctuation_style": "minimal", "contraction_usage": 2, "pronoun_preference": "first-person", "passive_voice_frequency": 5, "rhetorical_question_usage": 7, "list_usage_tendency": 2, "personal_anecdote_inclusion": 8, "pop_culture_reference_frequency": 2, "technical_jargon_usage": 9, "parenthetical_aside_frequency": 2, "humor_sarcasm_usage": 1, "emotional_expressiveness": 5, "emphatic_device_usage": 2, "quotation_frequency": 1, "analogy_usage": 5, "sensory_detail_inclusion": 2, "onomatopoeia_usage": 1, "alliteration_frequency": 1, "word_length_preference": "varied", "foreign_phrase_usage": 1, "rhetorical_device_usage": 4, "statistical_data_usage": 1, "personal_opinion_inclusion": 7, "transition_usage": 6, "reader_question_frequency": 7, "imperative_sentence_usage": 1, "dialogue_inclusion": 1, "regional_dialect_usage": 1, "hedging_language_frequency": 5, "language_abstraction": "abstract", "personal_belief_inclusion": 7, "repetition_usage": 3, "subordinate_clause_frequency": 7, "verb_type_preference": "mixed", "sensory_imagery_usage": 1, "symbolism_usage": 2, "digression_frequency": 7, "formality_level": 4, "reflection_inclusion": 7, "irony_usage": 1, "neologism_frequency": 1, "ellipsis_usage": 1, "cultural_reference_inclusion": 3, "stream_of_consciousness_usage": 8, "psychological_traits": { "openness_to_experience": 8, "conscientiousness": 5, "extraversion": 3, "agreeableness": 4, "emotional_stability": 5, "dominant_motivations": "achievement, power", "core_values": "knowledge, control", "decision_making_style": "analytical", "empathy_level": 5, "self_confidence": 7, "risk_taking_tendency": 6, "idealism_vs_realism": "realistic", "conflict_resolution_style": "assertive", "relationship_orientation": "independent", "emotional_response_tendency": "calm", "creativity_level": 8 }, "age": "25-35", "gender": "Not specified", "education_level": "Bachelor's degree in a technical field", "professional_background": "AI/ML data annotator at Meta", "cultural_background": "Not specified", "primary_language": "English", "language_fluency": "Fluent", "background": "The author is a professional working at Meta on AI/ML projects involving video annotation and neural networks. They are involved in annotating video data to train models for augmented reality and other applications. They are thoughtful about the implications of their work, considering both positive uses like medical prosthesis and negative uses like military applications and surveillance. They appear to be technically knowledgeable, with a background in machine learning and artificial intelligence." } ``` Setting up a custom FastAPI server to run Qwen2.5-Coder-1.5B locally for real-time code completion allows you to create a flexible, controlled environment for handling long contexts. Here’s how to set up this server and manage the context effectively. Step 1: Set Up Qwen2.5-Coder Locally First, make sure Qwen2.5-Coder-1.5B is set up and accessible on your system: Download and Configure Qwen2.5-Coder-1.5B: If it’s hosted on Hugging Face, use their model hub or a Docker image (if available) to download and set up the model locally. Install Dependencies: Ensure you have transformers, torch, and other dependencies required to run Qwen. You can install these with: bash Copy code pip install transformers torch fastapi uvicorn Quantization (Optional): Apply GPTQ or another quantization method if needed to optimize the model’s memory and speed without sacrificing too much quality. Step 2: Create a FastAPI Server This server will act as an interface for making real-time inference requests to the Qwen2.5-Coder model. Initialize FastAPI App: Create a new Python file, say main.py, and import FastAPI and the necessary libraries. Load the Model: Load Qwen2.5-Coder into memory when the FastAPI app starts. This way, it’s ready to serve requests immediately. python Copy code from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() # Load model and tokenizer globally model_name = "Qwen/Qwen-2.5-Coder-1.5B" # replace with the local path if downloaded tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() # Set the device to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) Step 3: Define an API Endpoint for Code Completion Define a route that takes code as input, handles long contexts, and returns completions. Chunking/Sliding Windows: To handle long contexts (e.g., 500-1000 lines), split the input into chunks of a size that fits within the model's maximum token limit. Processing the Request: When a request is received, the server tokenizes the input, manages the context, and generates a response. python Copy code @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: # Tokenize input prompt inputs = tokenizer(prompt, return_tensors="pt").to(device) # Handle long context with sliding windows input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions # max token limit of the model if input_length > max_token_length: # Sliding window mechanism context_size = max_token_length - max_length # Leave space for completion prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: # If the input is within limit, use it as is inputs = inputs # Generate code completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # Adjust sampling temperature if desired ) # Decode and return the generated text completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) Step 4: Run the Server Start the FastAPI server locally using Uvicorn: bash Copy code uvicorn main:app --host 0.0.0.0 --port 8000 The server will be accessible at http://localhost:8000, and you can test the /complete_code/ endpoint by sending a POST request with the code prompt. Step 5: Handling Real-Time Requests and Context Management To handle real-time completions, consider integrating the following strategies: Context Management: For long input, use a sliding window to keep the most relevant part of the context. This ensures that only the necessary code lines are passed to the model, preserving memory and speed. Caching Responses: To further optimize real-time performance, consider caching previous completions if you expect repeated requests with similar prompts. Step 6: Testing and Integrating with Your IDE You can test this setup by using an HTTP client like curl or Postman: bash Copy code curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 50}' For IDE integration, you can create a plugin or extension in your code editor (e.g., VS Code) that sends code snippets to this API for real-time code completion. This setup gives you full control over Qwen’s code completion behavior and allows you to fine-tune it for your needs without relying on external, commercial solutions.

Certainly, here’s a structured approach to implementing a FastAPI server that integrates Qwen2.5-Coder-1.5B for real-time code completion: ### Step 1: Setting Up Qwen2.5-Coder Locally 1. **Download and Install Qwen2.5-Coder**: - If Qwen2.5-Coder-1.5B is hosted on Hugging Face or another platform, use their model hub or Docker to download it locally. - Install necessary dependencies like `transformers`, `torch`, `fastapi`, and `uvicorn`: ```bash pip install transformers torch fastapi uvicorn ``` 2. **Optional Quantization**: - For optimization, you may apply quantization (e.g., GPTQ or other techniques) to reduce model memory usage while maintaining performance. ### Step 2: Creating the FastAPI Server FastAPI will handle inference requests by allowing you to define an API that sends prompts to Qwen2.5-Coder and receives code completion responses. 1. **Initialize FastAPI App**: - Create a file named `main.py` and initialize the app. - Load Qwen2.5-Coder and the tokenizer into memory so they’re ready to process requests. ```python from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() model_name = "Qwen/Qwen-2.5-Coder-1.5B" # replace with local path if downloaded tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ``` ### Step 3: Define an Endpoint for Code Completion This endpoint will accept code as input, handle the context, and return a completion. 1. **Implement a Sliding Window for Long Contexts**: - The model has a token limit, so for long inputs (500-1000 lines), use a sliding window approach to keep only the most relevant part of the input. ```python @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: inputs = tokenizer(prompt, return_tensors="pt").to(device) input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions if input_length > max_token_length: # Keep only the most recent context within max_token_length context_size = max_token_length - max_length prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: inputs = inputs # Generate the completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # Adjust as needed ) completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) ``` ### Step 4: Running the FastAPI Server Start the server using Uvicorn to make it accessible at `http://localhost:8000`: ```bash uvicorn main:app --host 0.0.0.0 --port 8000 ``` ### Step 5: Context Management and Real-Time Optimization 1. **Context Management with Sliding Windows**: - For long inputs, use a sliding window to keep the context manageable. This approach maintains relevant code sections while avoiding memory overflow. 2. **Response Caching**: - If you have requests with similar prompts, caching responses can save time by reducing redundant processing. ### Step 6: Testing and IDE Integration 1. **Testing**: - Use `curl` or Postman to test the `/complete_code/` endpoint. ```bash curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 50}' ``` 2. **IDE Integration**: - Develop a plugin or script for your IDE (like VS Code) to send code snippets to the FastAPI server and receive completions in real-time. ### Additional Features and Tuning - **Temperature and Sampling**: - Adjust the sampling temperature in `model.generate()` for different completion behaviors. - **API Timeout and Error Handling**: - Implement timeouts and additional error handling for better reliability during high request volumes. This setup lets you leverage Qwen’s capabilities for real-time completions tailored to your development environment, independent of external platforms.

help me do this: You could create a FastAPI server: from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() model_name = "Qwen/Qwen-2.5-Coder-1.5B" # replace with local path if downloaded tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) Then you could define an endpoint for code completion. @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: inputs = tokenizer(prompt, return_tensors="pt").to(device) input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions if input_length > max_token_length: # Keep only the most recent context within max_token_length context_size = max_token_length - max_length prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: inputs = inputs # Generate the completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # Adjust as needed ) completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e))

Here’s how to set up your FastAPI server to run Qwen2.5-Coder for code completion. Follow these steps to get everything up and running. ### Step 1: Install Required Packages Make sure you have `fastapi`, `uvicorn`, `transformers`, and `torch` installed. ```bash pip install fastapi uvicorn transformers torch ``` ### Step 2: Set Up the FastAPI Server Create a Python file, such as `main.py`, and add the following code to initialize the FastAPI app and load the model. ```python from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() # Specify the model name or path model_name = "Qwen/Qwen-2.5-Coder-1.5B" # replace with local path if downloaded # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() # Set the device to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ``` ### Step 3: Define the Code Completion Endpoint The endpoint will take a prompt, process it, and return a code completion response. This example also manages context for longer inputs by truncating to the most recent relevant lines when needed. ```python @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt").to(device) input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions # model's maximum token capacity # Check if the input exceeds the token limit if input_length > max_token_length: # Retain only the most recent tokens within the max_token_length context_size = max_token_length - max_length # leave room for completion prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: inputs = inputs # Generate the completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # adjust temperature for randomness ) # Decode the generated text completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) ``` ### Step 4: Run the FastAPI Server Use Uvicorn to start the FastAPI server. ```bash uvicorn main:app --host 0.0.0.0 --port 8000 ``` ### Step 5: Test the Endpoint To test the `/complete_code/` endpoint, you can use `curl` or a tool like Postman. Here’s an example using `curl`: ```bash curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 50}' ``` ### Summary of Key Points - **Device Management**: Ensures the model uses GPU if available. - **Context Management**: Trims long inputs to fit within the model’s max token limit using a sliding window approach. - **Real-time Inference**: Provides real-time completions via FastAPI, which you can integrate with your IDE or other applications. This setup should allow for a flexible and efficient code completion server tailored to your local environment! Let me know if you encounter any issues.

(venv) daniel@Daniels-MacBook-Pro Qwen % uvicorn main:app --host 0.0.0.0 --port 8000 A module that was compiled using NumPy 1.x cannot be run in NumPy 2.1.3 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 640, in run_until_complete self.run_forever() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 607, in run_forever self._run_once() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 1922, in _run_once handle._run() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/Users/daniel/Qwen/main.py", line 2, in <module> from transformers import AutoTokenizer, AutoModelForCausalLM File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/__init__.py", line 26, in <module> from . import dependency_versions_check File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/dependency_versions_check.py", line 16, in <module> from .utils.versions import require_version, require_version_core File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/__init__.py", line 27, in <module> from .chat_template_utils import DocstringParsingException, TypeHintParsingException, get_json_schema File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/chat_template_utils.py", line 39, in <module> from torch import Tensor File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/__init__.py", line 1477, in <module> from .functional import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), Traceback (most recent call last): File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status response.raise_for_status() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/requests/models.py", line 1024, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/Qwen/Qwen-2.5-Coder-1.5B/resolve/main/tokenizer_config.json The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/hub.py", line 403, in cached_file resolved_file = hf_hub_download( ^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 862, in hf_hub_download return _hf_hub_download_to_cache_dir( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 969, in _hf_hub_download_to_cache_dir _raise_on_head_call_error(head_call_error, force_download, local_files_only) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1484, in _raise_on_head_call_error raise head_call_error File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1376, in _get_metadata_or_catch_error metadata = get_hf_file_metadata( ^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1296, in get_hf_file_metadata r = _request_wrapper( ^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 277, in _request_wrapper response = _request_wrapper( ^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 301, in _request_wrapper hf_raise_for_status(response) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 454, in hf_raise_for_status raise _format(RepositoryNotFoundError, message, response) from e huggingface_hub.errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-67368cd7-356272951979b7f825444f8d;71514938-df7a-4efa-b5d3-8b6efc3045bd) Repository Not Found for url: https://huggingface.co/Qwen/Qwen-2.5-Coder-1.5B/resolve/main/tokenizer_config.json. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. Invalid username or password. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) ^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 690, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/daniel/Qwen/main.py", line 11, in <module> tokenizer = AutoTokenizer.from_pretrained(model_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 857, in from_pretrained tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 689, in get_tokenizer_config resolved_config_file = cached_file( ^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/hub.py", line 426, in cached_file raise EnvironmentError( OSError: Qwen/Qwen-2.5-Coder-1.5B is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' If this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>`

The error you're encountering is related to the Hugging Face API requesting a model that requires authentication. Here's a breakdown of the issue and how to resolve it: ### Key Errors: 1. **401 Unauthorized Error**: The Hugging Face Hub is refusing access to the model (`Qwen/Qwen-2.5-Coder-1.5B`) because it requires authentication. 2. **OSError**: The error message indicates that the model cannot be found either locally or remotely under the specified identifier. ### Solutions: 1. **Authenticate with Hugging Face**: - You need to log in to Hugging Face to access the model if it is a private or gated model. You can log in using your Hugging Face credentials by running the following command in your terminal: ```bash huggingface-cli login ``` - After logging in, try running your code again. 2. **Check Model Availability**: - Double-check if the model `Qwen/Qwen-2.5-Coder-1.5B` is publicly available on Hugging Face or if it requires access to a specific repository (e.g., private access). - If it's private, you need the correct permissions to access it. 3. **Use a Public Model or Local Copy**: - If you're trying to use this model from Hugging Face, ensure that the model is publicly available. If not, you can either request access or download a local copy of the model and load it directly from the local path. - For example, you can download the model files manually from Hugging Face and point to the local folder using `AutoTokenizer.from_pretrained("path_to_local_folder")`. 4. **Update Dependencies**: - Make sure all your libraries are up to date, especially `transformers`, `torch`, and `huggingface_hub`. Sometimes, compatibility issues can cause errors with downloading models. ```bash pip install --upgrade transformers torch huggingface_hub ``` Once you've completed these steps, try running your app again. If the model requires access to a private repository, you may need to ensure that you're authenticated correctly, or consider switching to a different model if the issue persists.

(venv) daniel@Daniels-MacBook-Pro Qwen % uvicorn main:app --host 0.0.0.0 --port 8000 A module that was compiled using NumPy 1.x cannot be run in NumPy 2.1.3 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 640, in run_until_complete self.run_forever() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 607, in run_forever self._run_once() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 1922, in _run_once handle._run() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/Users/daniel/Qwen/main.py", line 2, in <module> from transformers import AutoTokenizer, AutoModelForCausalLM File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/__init__.py", line 26, in <module> from . import dependency_versions_check File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/dependency_versions_check.py", line 16, in <module> from .utils.versions import require_version, require_version_core File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/__init__.py", line 27, in <module> from .chat_template_utils import DocstringParsingException, TypeHintParsingException, get_json_schema File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/chat_template_utils.py", line 39, in <module> from torch import Tensor File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/__init__.py", line 1477, in <module> from .functional import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) ^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 690, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/daniel/Qwen/main.py", line 12, in <module> model = AutoModelForCausalLM.from_pretrained(model_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained return model_class.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/modeling_utils.py", line 3657, in from_pretrained hf_quantizer.validate_environment( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/quantizers/quantizer_awq.py", line 50, in validate_environment raise ImportError("Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)") ImportError: Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)

The error message suggests that the model you're trying to load requires the `auto-awq` library, which is not installed in your environment. To resolve this, you can install the required library by running the following command: ```bash pip install autoawq ``` This should enable you to load the AWQ quantized model successfully. Once the installation is complete, try running your `uvicorn` command again. Let me know if you encounter any further issues!

no matching distribution found for autoawq

It seems that the `autoawq` library isn't available via PyPI. If you're trying to load a quantized model, it's possible that the library you're using relies on specific packages or models that are either hosted elsewhere or have different installation instructions. Here are a few steps you can try to resolve the issue: 1. **Check the Model Type**: Ensure that the model you're trying to load is compatible with the current environment and doesn't require a separate library that isn't available via PyPI. Double-check the documentation or the source from where the model is coming. 2. **Manual Installation of Quantization Tools**: Sometimes, specific quantization libraries like AWQ (Auto-weight quantization) might not have an easy installation method via `pip install`. In that case, the repository might provide an alternative method to install the necessary files. You might need to clone the repo or download and install it manually. 3. **Alternative Quantization Libraries**: If the quantization library isn't available, you might consider using other quantization libraries like Hugging Face's native quantization or libraries like `bitsandbytes` or `quantlib` if supported by the model you're using. To investigate more: - You can check the official [Hugging Face documentation](https://huggingface.co/docs/transformers/main/en/quantization) for how to work with quantized models. - If it's a specific third-party repository, try reaching out to their support or check for issues related to the `autoawq` package. Let me know if you'd like further guidance on any of these steps!

from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() # Specify the model name or path model_name = "/Users/daniel/Qwen/Qwen2.5-Coder-1.5B" # replace with local path if downloaded # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() # Set the device to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt").to(device) input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions # model's maximum token capacity # Check if the input exceeds the token limit if input_length > max_token_length: # Retain only the most recent tokens within the max_token_length context_size = max_token_length - max_length # leave room for completion prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: inputs = inputs # Generate the completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8 # adjust temperature for randomness ) # Decode the generated text completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=str(e))

Your FastAPI code looks well-structured for creating a code completion API using a local transformer model. However, there are a few small adjustments that can help ensure it works smoothly and avoids potential issues: ### Key Improvements: 1. **Input validation**: The prompt length check you implemented is great for handling long inputs, but make sure to explicitly check if the `max_length` provided by the user is within bounds. Sometimes, a larger `max_length` can push you beyond the model's capacity, leading to an error. 2. **Torch device handling**: The way you've set the device (`cuda` if available or `cpu`) is correct. Just ensure that your environment has GPU support if you intend to use it. 3. **Handling the `model.generate` method**: The `model.generate()` method can be a bit sensitive to its parameters. Specifically, `max_length` should be carefully set to avoid exceeding the model's limits. Here's an updated version of your code with a couple of refinements: ```python from fastapi import FastAPI, HTTPException from transformers import AutoTokenizer, AutoModelForCausalLM import torch app = FastAPI() # Specify the model name or path model_name = "/Users/daniel/Qwen/Qwen2.5-Coder-1.5B" # replace with local path if downloaded # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.eval() # Set the device to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) @app.post("/complete_code/") async def complete_code(prompt: str, max_length: int = 150): try: # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt").to(device) input_length = inputs.input_ids.size(1) max_token_length = model.config.n_positions # model's maximum token capacity # Check if the input exceeds the token limit if input_length > max_token_length: # Retain only the most recent tokens within the max_token_length context_size = max_token_length - max_length # leave room for completion prompt_tokens = inputs.input_ids[0, -context_size:] inputs = {"input_ids": prompt_tokens.unsqueeze(0).to(device)} else: # No truncation needed, use the full input inputs = inputs # Generate the completion with torch.no_grad(): outputs = model.generate( **inputs, max_length=input_length + max_length, do_sample=True, temperature=0.8, # adjust temperature for randomness pad_token_id=tokenizer.eos_token_id # Ensure padding works correctly ) # Decode the generated text completion = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"completion": completion} except Exception as e: raise HTTPException(status_code=500, detail=f"Error: {str(e)}") ``` ### Explanation of changes: 1. **Device handling**: Ensured that all inputs and models are explicitly moved to the device (either `cuda` or `cpu`). 2. **Padding token handling**: Added `pad_token_id=tokenizer.eos_token_id` to ensure padding works correctly during token generation. Some models (especially non-GPT ones) might not behave as expected without a valid `pad_token_id`. 3. **Error Handling**: The error message is now a bit more descriptive, helping you debug the issue if it happens. ### Next Steps: - **Testing**: Run the FastAPI server and test the `/complete_code/` endpoint using POST requests with a code prompt and optional `max_length`. - **Optimization**: If needed, you could further optimize the `temperature` or `max_length` based on your specific use case. Let me know if you need any further adjustments!

(venv) daniel@Daniels-MacBook-Pro Qwen % uvicorn main:app --host 0.0.0.0 --port 8000 A module that was compiled using NumPy 1.x cannot be run in NumPy 2.1.3 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 640, in run_until_complete self.run_forever() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 607, in run_forever self._run_once() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 1922, in _run_once handle._run() File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/Users/daniel/Qwen/main.py", line 2, in <module> from transformers import AutoTokenizer, AutoModelForCausalLM File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/__init__.py", line 26, in <module> from . import dependency_versions_check File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/dependency_versions_check.py", line 16, in <module> from .utils.versions import require_version, require_version_core File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/__init__.py", line 27, in <module> from .chat_template_utils import DocstringParsingException, TypeHintParsingException, get_json_schema File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/utils/chat_template_utils.py", line 39, in <module> from torch import Tensor File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/__init__.py", line 1477, in <module> from .functional import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /Users/daniel/Qwen/venv/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), Traceback (most recent call last): File "/Users/daniel/Qwen/venv/bin/uvicorn", line 8, in <module> sys.exit(main()) ^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 412, in main run( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/main.py", line 579, in run server.run() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 65, in run return asyncio.run(self.serve(sockets=sockets)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve await self._serve(sockets) File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/server.py", line 76, in _serve config.load() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/config.py", line 434, in load self.loaded_app = import_from_string(self.app) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/uvicorn/importer.py", line 19, in import_from_string module = importlib.import_module(module_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 690, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/daniel/Qwen/main.py", line 12, in <module> model = AutoModelForCausalLM.from_pretrained(model_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained return model_class.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/modeling_utils.py", line 3657, in from_pretrained hf_quantizer.validate_environment( File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/transformers/quantizers/quantizer_awq.py", line 50, in validate_environment raise ImportError("Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)") ImportError: Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`) (venv) daniel@Daniels-M

The error you're encountering indicates that the model you're trying to load requires the `auto-awq` library for quantization support. This happens because the model you're using has been quantized with the AWQ technique, and the transformers library is detecting this requirement. To fix this, you need to install the required library. You can do this by running the following command: ```bash pip install autoawq ``` Once installed, try running your FastAPI application again. This should resolve the `ImportError` and allow the model to load correctly. Let me know if you encounter any other issues!

(venv) daniel@Daniels-MacBook-Pro Qwen % python3 main.py /Users/daniel/Qwen/main.py:78: DeprecationWarning: on_event is deprecated, use lifespan event handlers instead. Read more about it in the [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/). @app.on_event("startup") INFO: Started server process [26559] INFO: Waiting for application startup. INFO:__main__:Using MPS device INFO:__main__:AWQ not available, loading standard model... ERROR:__main__:Error initializing model: Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`) ERROR:__main__:Failed to initialize model. Shutting down. ERROR: Traceback (most recent call last): File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 693, in lifespan async with self.lifespan_context(app) as maybe_state: File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 569, in __aenter__ await self._router.startup() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 670, in startup await handler() File "/Users/daniel/Qwen/main.py", line 82, in startup_event sys.exit(1) SystemExit: 1 ERROR: Application startup failed. Exiting.

The error message indicates that the model you're trying to load is an AWQ quantized model, and the application is failing to initialize because it cannot find the `auto-awq` library. Even though you've tried installing `autoawq`, the error suggests that the library might still not be available in your environment or that your FastAPI application is trying to use a model that's been quantized but can't find the necessary components to load it. Here's what you can do to resolve the issue: 1. **Ensure `auto-awq` is installed properly**: Sometimes, installation might not go through as expected. You can try reinstalling it to make sure the library is available in your virtual environment. Run: ```bash pip uninstall autoawq pip install autoawq ``` Then, verify that it's correctly installed by running: ```bash pip show autoawq ``` 2. **Fallback to standard model loading**: Your application seems to be attempting to load a regular (non-quantized) version of the model if AWQ is not available. It might be necessary to ensure the model you are using is either not quantized or that the appropriate fallback mechanism is working correctly. 3. **Check MPS Device Compatibility**: It seems like you're using an MPS (Metal Performance Shaders) device on a Mac (likely with an Apple Silicon chip). If the quantized model requires specific hardware acceleration (e.g., CUDA for NVIDIA GPUs), it might not work correctly with MPS. You can try loading the model without using the MPS device or attempt to load it on a CPU instead. Modify the device setup to use CPU instead of MPS: ```python device = torch.device("cpu") # Force the use of CPU model.to(device) ``` 4. **Model Quantization Check**: If you're trying to use a specific quantized model, ensure that the quantization steps were completed correctly during model preparation. It may be helpful to download a non-quantized version if available. Once you've made these changes, try running the application again. Let me know if you encounter any more issues!

(venv) daniel@Daniels-MacBook-Pro Qwen % python3 main.py /Users/daniel/Qwen/main.py:78: DeprecationWarning: on_event is deprecated, use lifespan event handlers instead. Read more about it in the [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/). @app.on_event("startup") INFO: Started server process [26642] INFO: Waiting for application startup. INFO:__main__:Using MPS device INFO:__main__:AWQ not available, loading standard model... ERROR:__main__:Error initializing model: Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`) ERROR:__main__:Failed to initialize model. Shutting down. ERROR: Traceback (most recent call last): File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 693, in lifespan async with self.lifespan_context(app) as maybe_state: File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 569, in __aenter__ await self._router.startup() File "/Users/daniel/Qwen/venv/lib/python3.11/site-packages/starlette/routing.py", line 670, in startup await handler() File "/Users/daniel/Qwen/main.py", line 82, in startup_event sys.exit(1) SystemExit: 1 ERROR: Application startup failed. Exiting.

The error persists, indicating that the application is failing to initialize due to the absence of the `auto-awq` library required for loading the AWQ quantized model. Let's go through a few potential steps to resolve the issue: ### 1. **Ensure `auto-awq` is installed correctly** It appears the library is still not being recognized. Double-check that it is installed within your virtual environment: ```bash pip show autoawq ``` If it doesn't show up, try reinstalling: ```bash pip uninstall autoawq pip install autoawq ``` If the installation is successful, verify that it is installed in the correct environment by running: ```bash which python ``` This will ensure that you are using the Python binary from your `venv` and not another Python installation. ### 2. **Check model loading code for AWQ quantized models** Since your code falls back to loading the standard model when `auto-awq` isn't available, ensure that the fallback path is functioning correctly. If you don’t need AWQ, you can force the model loading to use the standard model without checking for AWQ, bypassing the error. Modify your model loading code to avoid checking for AWQ and force the standard model to load: ```python try: model = load_awq_model() # or whatever method you're using for AWQ except Exception as e: print("AWQ model loading failed, using standard model instead.") model = load_standard_model() # fallback model loading function ``` ### 3. **Fallback to non-quantized model** If AWQ quantization is not essential for your project, try using a non-quantized version of the model. This would involve loading the original model instead of the quantized one. Ensure that the model initialization code is set to prefer the non-quantized model if AWQ is not available. The error handling or fallback mechanism might need to be more robust to handle missing libraries. ### 4. **Verify MPS (Metal Performance Shaders) Compatibility** If the MPS device (for Apple's GPU acceleration) is causing issues, you can try running your code on a CPU instead. In some cases, MPS may not be fully compatible with certain operations that quantized models require. You can explicitly set your model to run on CPU to test if this resolves the issue: ```python import torch device = torch.device('cpu') model = model.to(device) ``` ### 5. **Re-check the environment** Sometimes virtual environments can have issues with dependencies. You might want to try recreating the virtual environment: ```bash deactivate rm -rf venv python3 -m venv venv source venv/bin/activate pip install -r requirements.txt ``` This ensures that the environment is clean and all dependencies are freshly installed. --- Once you've tried these steps, let me know the results or if you encounter any additional errors!

daniel@Daniels-MacBook-Pro frontend % cd cd daniel@Daniels-MacBook-Pro ~ % cd qwen daniel@Daniels-MacBook-Pro qwen % curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 50}' {"detail":"Error during code completion: MPS backend out of memory (MPS allocated: 3.01 GB, other allocations: 448.49 MB, max allowed: 3.40 GB). Tried to allocate 256 bytes on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."}%

It looks like the error you're encountering is related to running out of memory on your Mac's GPU when using the MPS (Metal Performance Shaders) backend for PyTorch. The error message indicates that the GPU has hit its memory limit, and the model couldn't allocate more memory to continue. Here are a few potential solutions to address the "MPS backend out of memory" issue: ### 1. **Limit Memory Usage with `PYTORCH_MPS_HIGH_WATERMARK_RATIO`** As the error message suggests, you can disable the memory limit for MPS by setting the `PYTORCH_MPS_HIGH_WATERMARK_RATIO` environment variable to `0.0`. This will allow PyTorch to bypass the upper memory limit for MPS. You can set this variable before running the app like this: ```bash export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 python3 main.py ``` However, keep in mind that bypassing the memory limit could cause your system to run into issues, such as crashes, if the memory demand exceeds what your system can handle. ### 2. **Force the Model to Run on CPU** If you're not strictly dependent on the MPS GPU backend, you can try running the model on your CPU instead of the GPU. This will use system RAM, which may be more abundant than GPU memory. You can switch to CPU by setting the device to `cpu` in your PyTorch code: ```python import torch device = torch.device('cpu') model = model.to(device) ``` Then, run your code again and see if this resolves the issue. ### 3. **Reduce Model Size or Batch Size** Another approach is to reduce the memory usage by lowering the batch size or reducing the complexity of the model you're using. For example, if the model is a transformer-based model, you could try using a smaller model variant or reducing the `max_length` for code completion. Try reducing the `max_length` in your `curl` command: ```bash curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 20}' ``` This will decrease the memory requirement for each request. ### 4. **Increase GPU Memory (If Possible)** If you have access to a Mac with more GPU memory, this might help as well, but if you’re on a device with limited memory (like the 3.4 GB limit in your case), it may be difficult to exceed that limit without running into issues. If you're using an Apple M1/M2 chip, the GPU memory is shared with system memory, and you may have little control over it. ### 5. **Optimize the Model** If your model supports quantization or pruning, you might want to try a more optimized version of the model, such as using a smaller quantized model or pruning the model to save memory. --- Try one or more of these suggestions to resolve the memory issue. Let me know if you need more help with any of them!

daniel@Daniels-MacBook-Pro qwen % curl -X POST "http://localhost:8000/complete_code/" \ -H "Content-Type: application/json" \ -d '{"prompt": "def add(a, b):\n return ", "max_length": 20}' {"detail":"Error during code completion: MPS backend out of memory (MPS allocated: 3.01 GB, other allocations: 448.80 MB, max allowed: 3.40 GB). Tried to allocate 256 bytes on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."}% daniel@Daniels-MacBook-Pro qwen %

It seems like the MPS backend is still running out of memory even with the reduced `max_length`. You can try a few more things to work around this issue: ### 1. **Force PyTorch to Use CPU** If your GPU memory is too limited, switching to CPU for the code completion process might resolve the issue. You can do this by setting the device to CPU within your application code. In your `main.py` (or wherever the model is initialized), explicitly set the model to use the CPU instead of the MPS backend: ```python import torch device = torch.device('cpu') # Set to 'cpu' instead of 'mps' model = model.to(device) ``` After making this change, restart your server and test again. This should avoid running into GPU memory limits by using system RAM instead. ### 2. **Further Reduce Memory Consumption** Even with `max_length` set to 20, there may still be other factors consuming GPU memory. Try further reducing the size of the model or its configuration if possible. If your code supports it, you might experiment with different models or smaller versions of the current model. ### 3. **Use `PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0`** You can also try setting `PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0` to bypass the memory cap altogether, but remember that this can lead to system instability if the memory demand exceeds the available resources. To set this, run: ```bash export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 python3 main.py ``` Then test the code again. This would only be advisable if you have a sufficient amount of system RAM and are okay with the potential for system instability. ### 4. **Optimize or Quantize the Model** If you're not already using a quantized model, consider using a smaller, optimized, or quantized version of the model to reduce memory consumption. This can often significantly reduce the GPU memory requirements. Let me know how these changes work or if you'd like more help with further troubleshooting!

1. What does the modality label help determine? * 5 points The language of the query The need for image context The length of the response The tone of the response 2. When is a query considered a "text" query? * 5 points When it requires image context When it does not require image context When it is longer than 100 words When it is a follow-up question 3. What does the "True Follow Up" label indicate? * 5 points The grammatical correctness of the response The logical connection to the previous turn The presence of an image in the query The length of the dialog 4. What does the "Need Previous Image" label signify? * 5 points The query requires a new image The query requires context from a previous image The query is a true follow up Both B & C 5. Which domain would a question about the calorie count of a food item fall under? * 5 points Local Food Gardening General products 6. What is the domain for a query asking about the ethnicity of a person in an image? * 5 points People Health Text Books 7. What category type should be used for a request to translate a phrase? * 5 points Action Chat Factual Utility 8. What is the correct response accuracy label for a factually correct and complete answer? * 5 points Factual-missing Factual-unacceptable Factual-acceptable Factual-perfect 9. What action stage is indicated when a user changes the trigger time of a scheduled reminder they set in the previous turn? * 5 points New action Action Continuation Action Completion Action Termination 10. Which category type involves requests for information that changes frequently? * 5 points Factual static Factual slow-changing Factual dynamic Factual recognition 11. What is the domain for a query about booking a table at a restaurant? * 5 points Local Reservation Food Shopping availability 12. What does the "Action Partially Correct" label indicate? * 5 points The system took no action The system took the correct action The system took an incorrect action The system requested to repeat information 13. What category should be used for a request to perform a mathematical calculation? * 5 points Action Chat Factual Utility 14. What is the appropriate response accuracy label for a response with minor factual errors? * 5 points Factual-missing Factual-unacceptable Factual-acceptable Factual-perfect 15. What is the domain for a query asking for the translation of a word? * 5 points Text Translation Books Chat 16. What is the appropriate domain for a query about the opening hours of a local restaurant? * 5 points Food Local Shopping availability Time 17. Which domain should be used for a query about the author of a book? * 5 points Books Text General products People 18. What is the correct response accuracy grade if the assistant's response is outdated? * 5 points Factual-missing Factual-unacceptable Factual-acceptable Factual-perfect 19. Which domain applies to a user query about the safety of drinking water from a specific faucet? * 5 points Health Local Utility General products 20. Which domain should be used for a query about the price of a product at a specific store? * 5 points Shopping price Shopping availability General products Local

Here are the correct answers based on the context provided: 1. **What does the modality label help determine?** **The need for image context** 2. **When is a query considered a "text" query?** **When it does not require image context** 3. **What does the "True Follow Up" label indicate?** **The logical connection to the previous turn** 4. **What does the "Need Previous Image" label signify?** **Both B & C (The query requires context from a previous image, and it is a true follow-up)** 5. **Which domain would a question about the calorie count of a food item fall under?** **Food** 6. **What is the domain for a query asking about the ethnicity of a person in an image?** **People** 7. **What category type should be used for a request to translate a phrase?** **Utility** 8. **What is the correct response accuracy label for a factually correct and complete answer?** **Factual-perfect** 9. **What action stage is indicated when a user changes the trigger time of a scheduled reminder they set in the previous turn?** **Action Continuation** 10. **Which category type involves requests for information that changes frequently?** **Factual dynamic** 11. **What is the domain for a query about booking a table at a restaurant?** **Reservation** 12. **What does the "Action Partially Correct" label indicate?** **The system took the correct action** 13. **What category should be used for a request to perform a mathematical calculation?** **Utility** 14. **What is the appropriate response accuracy label for a response with minor factual errors?** **Factual-acceptable** 15. **What is the domain for a query asking for the translation of a word?** **Translation** 16. **What is the appropriate domain for a query about the opening hours of a local restaurant?** **Local** 17. **Which domain should be used for a query about the author of a book?** **Books** 18. **What is the correct response accuracy grade if the assistant's response is outdated?** **Factual-unacceptable** 19. **Which domain applies to a user query about the safety of drinking water from a specific faucet?** **Health** 20. **Which domain should be used for a query about the price of a product at a specific store?** **Shopping price**