Instructions to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "M-Alkassem/qwen2.5-coder-3b-unsloth-lora")

Transformers

How to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with Transformers:

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

pipe = pipeline("text-generation", model="M-Alkassem/qwen2.5-coder-3b-unsloth-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("M-Alkassem/qwen2.5-coder-3b-unsloth-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "M-Alkassem/qwen2.5-coder-3b-unsloth-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": "M-Alkassem/qwen2.5-coder-3b-unsloth-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/M-Alkassem/qwen2.5-coder-3b-unsloth-lora

SGLang

How to use M-Alkassem/qwen2.5-coder-3b-unsloth-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 "M-Alkassem/qwen2.5-coder-3b-unsloth-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": "M-Alkassem/qwen2.5-coder-3b-unsloth-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 "M-Alkassem/qwen2.5-coder-3b-unsloth-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": "M-Alkassem/qwen2.5-coder-3b-unsloth-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for M-Alkassem/qwen2.5-coder-3b-unsloth-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for M-Alkassem/qwen2.5-coder-3b-unsloth-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for M-Alkassem/qwen2.5-coder-3b-unsloth-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="M-Alkassem/qwen2.5-coder-3b-unsloth-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use M-Alkassem/qwen2.5-coder-3b-unsloth-lora with Docker Model Runner:
```
docker model run hf.co/M-Alkassem/qwen2.5-coder-3b-unsloth-lora
```

qwen2.5-coder-3b-unsloth-lora

This repository contains a LoRA adapter, not a full standalone model.

It was created by fine-tuning unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit for coding-assistance behavior on Google Colab using T4 GPU.

What This Model Is

This adapter is the first-stage coding-focused fine-tune in the project.

Training goal:

improve structured coding responses
improve instruction-following for programming tasks
improve simple bug-fixing behavior

This adapter should be loaded on top of the base model:

base model: unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit

Dataset

This adapter was trained on a sampled subset of:

bigcode/self-oss-instruct-sc2-exec-filter-50k

Project training setup:

sampled rows before filtering: 4000
rows used after filtering: 3993
max sequence length: 1024
training steps: 250

Training Summary

This model was trained with supervised fine-tuning (SFT) using LoRA and 4-bit quantization.

Key setup:

LoRA rank: 16
LoRA alpha: 16
LoRA dropout: 0
batch size per device: 1
gradient accumulation: 16
learning rate: 1e-4
optimizer: adamw_8bit
hardware: Google Colab Tesla T4

Observed result:

final training loss: about 0.6130

Intended Use

Use this adapter when you want:

a lightweight coding assistant
better structured code answers
simple debugging help
a PEFT adapter that runs on top of the Qwen2.5-Coder 3B base model

Limitations

This adapter is not a standalone merged model.

It also was not the strongest model in the later direct-answer benchmark on every prompt. It improved some focused coding-task behavior, but it should be understood as a practical low-resource experiment rather than a universally superior model.

How To Load

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

BASE_MODEL = "Qwen/Qwen2.5-Coder-3B-Instruct"
ADAPTER_MODEL = "M-Alkassem/qwen2.5-coder-3b-unsloth-lora"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    torch_dtype=torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
model.eval()

Example Prompt prompt = "Debug this Python code and explain the bug: def is_even(n): return n % 2 == 1"

Project Context This adapter is part of a larger two-stage project:

coding-focused adapter: this repository agent-oriented continued adapter: M-Alkassem/qwen2.5-coder-3b-agent-v1 The later agent adapter was trained by continuing from this coding adapter.

References