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vllm-project
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Qwen/Qwen3-1.7B
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This is a specialized code-generation model. It takes a textual description of a linear algebra problem and outputs a Python script, typically using the numpy library, that solves it.
Qwen/Qwen3-1.7BUse the code below to get started with the model. This example uses 4-bit quantization for efficient inference on consumer hardware.
python
# First, install necessary libraries# !pip install transformers torch peft accelerate bitsandbytes import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig BASE_MODEL_ID = "Qwen/Qwen3-1.7B"ADAPTER_MODEL_ID = "premjatin/qwen-linear-algebra-coder" quantization_config = BitsAndBytesConfig(load_in_4bit=True) # Load the base model and tokenizerbase_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, quantization_config=quantization_config,)tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_token # Load the LoRA adaptermodel = PeftModel.from_pretrained(base_model, ADAPTER_MODEL_ID) # --- Define a problem ---problem_description = """Find the eigenvalues and eigenvectors of the following 3x3 matrix:[[4, 0, 1], [-2, 1, 0], [-2, 0, 1]]""" # --- Create a structured prompt ---prompt = f"""### INSTRUCTION:You are an AI assistant that generates Python code to solve linear algebra problems. ### PROBLEM:{problem_description} ### PYTHON SOLUTION:inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=256, do_sample=False, )
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response_text)
text
Training Details Training Data The model was fine-tuned on a custom dataset of approximately 10,000 problem-and-solution pairs related to linear algebra. The data was structured to teach the model how to convert a word problem into a Python script.Training HyperparametersThe model was trained using the PEFT library with the following LoRA configuration: { "r": 16, "lora_alpha": 32, "lora_dropout": 0.05, "bias": "none", "task_type": "CAUSAL_LM", "target_modules": [ "down_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "q_proj", "o_proj" ]} Bias, Risks, and Limitations Code Correctness: The generated code is not guaranteed to be 100% correct or optimal. Always review and test the code before using it in a production environment. Scope: The model's capabilities are limited to the types of problems present in its training data. It may not perform well on highly complex or novel problems. Library Bias: The model was primarily trained on solutions using numpy and will heavily favor it in its responses.# First, install necessary libraries# !pip install transformers torch peft accelerate bitsandbytes import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig BASE_MODEL_ID = "Qwen/Qwen3-1.7B"ADAPTER_MODEL_ID = "premjatin/qwen-linear-algebra-coder" quantization_config = BitsAndBytesConfig(load_in_4bit=True) # Load the base model and tokenizerbase_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, quantization_config=quantization_config,)tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_token # Load the LoRA adaptermodel = PeftModel.from_pretrained(base_model, ADAPTER_MODEL_ID) # --- Define a problem ---problem_description = """Find the eigenvalues and eigenvectors of the following 3x3 matrix:[[4, 0, 1], [-2, 1, 0], [-2, 0, 1]]""" # --- Create a structured prompt ---prompt = f"""### INSTRUCTION:You are an AI assistant that generates Python code to solve linear algebra problems. ### PROBLEM:{problem_description} ### PYTHON SOLUTION:Training DetailsTraining DataThe model was fine-tuned on a custom dataset of approximately 10,000 problem-and-solution pairs related to linear algebra. The data was structured to teach the model how to convert a word problem into a Python script.Training HyperparametersThe model was trained using the PEFT library with the following LoRA configuration:{ "r": 16, "lora_alpha": 32, "lora_dropout": 0.05, "bias": "none", "task_type": "CAUSAL_LM", "target_modules": [ "down_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "q_proj", "o_proj" ]}Bias, Risks, and LimitationsCode Correctness: The generated code is not guaranteed to be 100% correct or optimal. Always review and test the code before using it in a production environment.Scope: The model's capabilities are limited to the types of problems present in its training data. It may not perform well on highly complex or novel problems.Library Bias: The model was primarily trained on solutions using numpy and will heavily favor it in its responses.