Instructions to use kamranrafi/Qwen2.5_Coder_14B_CodingModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kamranrafi/Qwen2.5_Coder_14B_CodingModel with Transformers:

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

pipe = pipeline("text-generation", model="kamranrafi/Qwen2.5_Coder_14B_CodingModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kamranrafi/Qwen2.5_Coder_14B_CodingModel")
model = AutoModelForCausalLM.from_pretrained("kamranrafi/Qwen2.5_Coder_14B_CodingModel", device_map="auto")
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 kamranrafi/Qwen2.5_Coder_14B_CodingModel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kamranrafi/Qwen2.5_Coder_14B_CodingModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kamranrafi/Qwen2.5_Coder_14B_CodingModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kamranrafi/Qwen2.5_Coder_14B_CodingModel

SGLang

How to use kamranrafi/Qwen2.5_Coder_14B_CodingModel 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 "kamranrafi/Qwen2.5_Coder_14B_CodingModel" \
    --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": "kamranrafi/Qwen2.5_Coder_14B_CodingModel",
		"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 "kamranrafi/Qwen2.5_Coder_14B_CodingModel" \
        --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": "kamranrafi/Qwen2.5_Coder_14B_CodingModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use kamranrafi/Qwen2.5_Coder_14B_CodingModel 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 kamranrafi/Qwen2.5_Coder_14B_CodingModel 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 kamranrafi/Qwen2.5_Coder_14B_CodingModel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kamranrafi/Qwen2.5_Coder_14B_CodingModel to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kamranrafi/Qwen2.5_Coder_14B_CodingModel",
    max_seq_length=2048,
)

Docker Model Runner
How to use kamranrafi/Qwen2.5_Coder_14B_CodingModel with Docker Model Runner:
```
docker model run hf.co/kamranrafi/Qwen2.5_Coder_14B_CodingModel
```

Qwen2.5_Coder_14B_CodingModel / README.md

kamranrafi

Update README.md

b971b43 verified 10 months ago

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	---
	base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	license: apache-2.0
	language:
	- zho
	- eng
	- fra
	- spa
	- por
	- deu
	- ita
	- rus
	- jpn
	- kor
	- vie
	- tha
	- ara
	datasets:
	- nvidia/OpenCodeReasoning
	---

	# Qwen2.5_Coder_14B_CodingModel

	Developer: `kamranrafi`
	Base model: `Qwen/Qwen2.5-Coder-14B-Instruct`
	Objective: Codegeneration with explanations.
	License: Apache-2.0
	Dataset: [`nvidia/OpenCodeReasoning`](https://huggingface.co/datasets/nvidia/OpenCodeReasoning)

	## Quick Inference

	### Transformers (PyTorch)

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

	model_id = "kamranrafi/Qwen2.5_Coder_14B_CodingModel"
	tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.float16,
	device_map="cuda:1"
	)

	def chat(user_msg, max_new_tokens=512, temperature=0.2, top_p=0.9):
	msgs = [
	{"role":"system","content": "You are Qwen2.5 Coder 14B Coding Model, a smart coding assistant.\n"},
	{"role":"user","content": user_msg},
	]
	prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
	inputs = tok(prompt, return_tensors="pt").to(model.device)
	out = model.generate(
	**inputs,
	max_new_tokens=max_new_tokens,
	temperature=temperature,
	top_p=top_p,
	do_sample=temperature > 0
	)
	text = tok.decode(out[0], skip_special_tokens=True)
	# Optional: trim everything before the assistant turn
	return text.split("<\|im_start\|>assistant")[-1].strip()

	print(chat("Create a function to return sorted list."))
	```

	## 🧾 Citation

	If you use this model, please cite:

	```
	@misc{
	title = {Qwen2.5_Coder_14B_CodingModel},
	author = {Muhammad Kamran Rafi},
	year = {2025},
	howpublished = {\url{https://huggingface.co/kamranrafi/Qwen2.5_Coder_14B_CodingModel}},
	note = {Fine-tuned with Unsloth on nvidia/OpenCodeReasoning}
	}
	```

	This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)