Instructions to use rishini/qwen2.5-coder-7b-programming-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rishini/qwen2.5-coder-7b-programming-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "rishini/qwen2.5-coder-7b-programming-lora")

Transformers

How to use rishini/qwen2.5-coder-7b-programming-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rishini/qwen2.5-coder-7b-programming-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rishini/qwen2.5-coder-7b-programming-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rishini/qwen2.5-coder-7b-programming-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rishini/qwen2.5-coder-7b-programming-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rishini/qwen2.5-coder-7b-programming-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rishini/qwen2.5-coder-7b-programming-lora

SGLang

How to use rishini/qwen2.5-coder-7b-programming-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rishini/qwen2.5-coder-7b-programming-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rishini/qwen2.5-coder-7b-programming-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rishini/qwen2.5-coder-7b-programming-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rishini/qwen2.5-coder-7b-programming-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rishini/qwen2.5-coder-7b-programming-lora with Docker Model Runner:
```
docker model run hf.co/rishini/qwen2.5-coder-7b-programming-lora
```

qwen2.5-coder-7b-programming-lora / scripts /README.md

rishini

Add training and evaluation scripts

f238825 verified 6 days ago

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Finetune + Eval Scripts

Scripts used to train and evaluate the adapters in this repo.

Environment

python3 -m venv /home/ai/llama-finetune-env
/home/ai/llama-finetune-env/bin/pip install torch transformers accelerate peft bitsandbytes trl datasets huggingface_hub

GPU note: GPU 0 is occupied by vLLM; both scripts pin CUDA_VISIBLE_DEVICES=1.

Programming model (this repo)

programming_finetune_aggressive.py — trains rishini/qwen2.5-coder-7b-programming-lora
- Base: Qwen/Qwen2.5-Coder-7B-Instruct, LoRA r=64 alpha=128, 6k filtered CodeAlpaca examples, 3 epochs, completion-only masking.
- Output dir: /home/ai/qwen-coder-programming-best
eval_best_model.py — held-out evaluation prompts, loads the adapter from /home/ai/qwen-coder-programming-best.

Security model (other work)

security_finetune.py — CodeLlama-7B-Instruct + LoRA for vulnerability analysis.
- Base: codellama/CodeLlama-7b-Instruct-hf, output dir: /home/ai/codellama-security-finetuned
test_security_model.py — runs the security adapter on sample vulnerable snippets.

Run

cd /home/ai
/home/ai/llama-finetune-env/bin/python programming_finetune_aggressive.py   # train
/home/ai/llama-finetune-env/bin/python eval_best_model.py                    # eval