Instructions to use SOULAMA/qwen2.5-coder-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SOULAMA/qwen2.5-coder-ft with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SOULAMA/qwen2.5-coder-ft")
model = AutoModelForCausalLM.from_pretrained("SOULAMA/qwen2.5-coder-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SOULAMA/qwen2.5-coder-ft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SOULAMA/qwen2.5-coder-ft

SGLang

How to use SOULAMA/qwen2.5-coder-ft 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 "SOULAMA/qwen2.5-coder-ft" \
    --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": "SOULAMA/qwen2.5-coder-ft",
		"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 "SOULAMA/qwen2.5-coder-ft" \
        --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": "SOULAMA/qwen2.5-coder-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SOULAMA/qwen2.5-coder-ft with Docker Model Runner:
```
docker model run hf.co/SOULAMA/qwen2.5-coder-ft
```

qwen2.5-coder-ft / README.md

SOULAMA

Update README.md

4e08d91 verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

4.72 kB

	---
	library_name: transformers
	tags: []
	---
	library_name: transformers
	tags:
	- qwen
	- code
	- text-generation
	- fine-tuned

	# Model Card for qwen2.5-coder-ft

	This model is a fine-tuned and merged version of Qwen2.5-Coder-1.5B-Instruct, specialized in Python programming and precise code generation.

	## Model Details

	### Model Description

	This model has been fine-tuned using Low-Rank Adaptation (LoRA) and subsequently merged into full 16-bit precision weights. It is optimized to act as a strict code assistant, delivering accurate programming solutions while minimizing conversational overhead.

	- Developed by: Soulama Haicanama Ismael
	- Model type: Causal Language Model (Transformer Architecture)
	- Language(s) (NLP): English, Python
	- License: Apache 2.0 (inherited from Qwen base model)
	- Finetuned from model: Qwen/Qwen2.5-Coder-1.5B-Instruct

	### Model Sources

	- Repository: SOULAMA/qwen2.5-coder-ft

	## Uses

	### Direct Use

	This model is intended for direct code generation and answering programming questions. It is designed to work within a Chat Template infrastructure using specific system prompts to isolate python code blocks.

	### Out-of-Scope Use

	The model should not be used for generic non-coding tasks (such as writing creative essays, general chat, or translation), as its attention layers have been heavily adjusted towards script structures and programmatic vocabulary.

	## Bias, Risks, and Limitations

	Due to its 1.5B parameter size, the model can suffer from context-loop repetition if the stopping criteria are not explicitly configured during inference. Users must handle stop tokens (`<\|im_end\|>`) strictly in their generation script to ensure execution stability.

	### Recommendations

	It is highly recommended to lower the generation temperature ($\le 0.2$) and provide clear, standalone system instructions to ensure deterministic code results.

	## How to Get Started with the Model

	Use the code below to get started with the model using proper generation boundaries:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	MODEL_ID = "SOULAMA/qwen2.5-coder-ft"
	device = "cuda" if torch.cuda.is_available() else "cpu"

	tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
	model = AutoModelForCausalLM.from_pretrained(
	MODEL_ID,
	torch_dtype=torch.float16,
	device_map="auto"
	)

	question = "Write a Python function that takes two values c and d and returns c+d."

	def build_prompt(question: str) -> str:
	return (
	"<\|im_start\|>system\n"
	"Tu es un expert en programmation. Écris uniquement le code Python qui résout le problème.\n"
	"<\|im_end\|>\n"
	"<\|im_start\|>user\n"
	f"{question}\n"
	"<\|im_end\|>\n"
	"<\|im_start\|>assistant\n"
	)

	messages=build_prompt(question)

	inputs = tokenizer(messages, add_generation_prompt=True, return_tensors="pt").to(device)

	with torch.no_grad():
	output_ids = model.generate(
	inputs,
	max_new_tokens=256,
	temperature=0.1,
	repetition_penalty=1.2,
	pad_token_id=tokenizer.eos_token_id,
	eos_token_id=tokenizer.eos_token_id
	)

	new_tokens = output_ids[0][inputs.shape[1]:]
	print(tokenizer.decode(new_tokens, skip_special_tokens=True))

	```

	## Training Details

	### Training Data

	The model was trained on a custom instruction dataset containing coding exercises, software engineering questions, and structured Python scripts.

	### Training Procedure

	#### Preprocessing

	Prompts were structured using the Qwen ChatML format, dividing blocks into `<\|im_start\|>system`, `<\|im_start\|>user`, and `<\|im_start\|>assistant` segments to maintain deep semantic alignment with the original instruct template.

	#### Training Hyperparameters

	* Training regime: PEFT (LoRA) followed by a full matrix `merge_and_unload()` into float16 precision.
	* Base model precision: 4-bit quantized base setup during training (BitsAndBytes).
	* Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj.

	#### Speeds, Sizes, Times

	* Checkpoint size: ~3.09 GB (Full Safetensors model)
	* Adaptation layer size: ~73.9 MB (LoRA Weights)

	## Technical Specifications

	### Model Architecture and Objective

	Based on the Qwen2.5-Coder dense architecture with Grouped-Query Attention (GQA) and RoPE (Rotary Position Embedding) optimized for dense source code token sequences.

	### Compute Infrastructure

	#### Hardware

	* GPU Type: 1 x NVIDIA Tesla T4 (via Google Colab Ecosystem)

	#### Software

	* Libraries: PyTorch, Transformers, PEFT, BitsAndBytes, TRL.

	## Model Card Authors
	```
	Soulama Haicanama Ismael
	```

	## Model Card Contact

	[More Information Needed]