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

A LoRA adapter fine-tuned on top of Qwen/Qwen2.5-Coder-7B-Instruct to produce clean, correct, efficient programming solutions with brief explanations.

Model Details

Base model: Qwen/Qwen2.5-Coder-7B-Instruct
Method: LoRA (rank 64, alpha 128, use_rslora=True)
Trainable params: 161,480,704 (~2.08% of total)
Data: 6,006 quality-filtered examples from iamtarun/python_code_instructions_18k_alpaca + curated expert-written seeds
Training: 3 epochs, effective batch size 32, max context 2048, completion-only label masking, cosine LR 2e-4, bf16 + 4-bit NF4 base, gradient checkpointing
Final train loss: 0.326

Usage

Load with PEFT:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter = "rishini/qwen2.5-coder-7b-programming-lora"

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    base, quantization_config=bnb, device_map="auto", torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(model, adapter)

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True, use_fast=True)

prompt = "Write a Python function to check if a string is a valid palindrome ignoring case and non-alphanumeric characters."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

Held-out prompts (not in the training set) answered correctly, including: longest common prefix, min-heap from scratch, topological sort, palindromic substrings (DP), and sliding-window longest substring.

Files

adapter_config.json / adapter_model.safetensors — LoRA weights
tokenizer.json / tokenizer_config.json / chat_template.jinja — tokenizer + chat template

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