---
title: qwen-linear-algebra-coder
canonical_url: "https://www.modelscope.cn/models/vllm-ascend/qwen-linear-algebra-coder"
md_url: "https://www.modelscope.cn/models/vllm-ascend/qwen-linear-algebra-coder.md"
repository: vllm-ascend/qwen-linear-algebra-coder
last_updated: 2026-03-18
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - Qwen/Qwen3-1.7B
base_model_relation: adapter
parameters: 17.4M
tensor_type:
  - F32
library_name:
  - pytorch
  - transformer
  - lora
  - peft
  - safetensors
downloads: 20574
stars: 0
tags:
  - lora
  - qwen
  - peft
  - code-generation
  - linear-algebra
  - python
---

# qwen-linear-algebra-coder

> qwen-linear-algebra-coder - vllm-ascend 在 ModelScope 开源的模型。qwen-linear-algebra-coder

vllm-ascend/qwen-linear-algebra-coder 是 ModelScope 魔搭社区上的 17.4M 参数text-generation模型，采用 mit 许可，基于 Qwen/Qwen3-1.7B 构建。

- **Repository**: vllm-ascend/qwen-linear-algebra-coder
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 17.4M
- **Base model**: Qwen/Qwen3-1.7B
- **Tags**: lora, qwen, peft, code-generation, linear-algebra, python
- **Downloads**: 20574
- **Stars**: 0
- **Last updated**: 2026-03-18

Source: https://www.modelscope.cn/models/vllm-ascend/qwen-linear-algebra-coder

---

# qwen-linear-algebra-coder

This repository contains a LoRA (Low-Rank Adaptation) fine-tuned version of `Qwen/Qwen3-1.7B`. This model is specialized for generating Python code to solve linear algebra problems described in natural language.

## Model Details

### Model Description

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.

- **Developed by:** [premjatin](https://huggingface.co/premjatin)
- **Model type:** LoRA fine-tune
- **Language(s):** English, Python
- **License:** MIT
- **Finetuned from model:** `Qwen/Qwen3-1.7B`

## How to Get Started with the Model

Use 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 torch
from peft import PeftModel
from 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 tokenizer
base_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 adapter
model = 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:
```


# --- Generate the 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)
```

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 Hyperparameters
The 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.