Instructions to use dbands/Qwen2.5-Coder-7B-Instruct-reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dbands/Qwen2.5-Coder-7B-Instruct-reason with Transformers:

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

pipe = pipeline("text-generation", model="dbands/Qwen2.5-Coder-7B-Instruct-reason")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dbands/Qwen2.5-Coder-7B-Instruct-reason")
model = AutoModelForCausalLM.from_pretrained("dbands/Qwen2.5-Coder-7B-Instruct-reason")
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

vLLM

How to use dbands/Qwen2.5-Coder-7B-Instruct-reason with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dbands/Qwen2.5-Coder-7B-Instruct-reason"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbands/Qwen2.5-Coder-7B-Instruct-reason",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dbands/Qwen2.5-Coder-7B-Instruct-reason

SGLang

How to use dbands/Qwen2.5-Coder-7B-Instruct-reason 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 "dbands/Qwen2.5-Coder-7B-Instruct-reason" \
    --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": "dbands/Qwen2.5-Coder-7B-Instruct-reason",
		"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 "dbands/Qwen2.5-Coder-7B-Instruct-reason" \
        --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": "dbands/Qwen2.5-Coder-7B-Instruct-reason",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use dbands/Qwen2.5-Coder-7B-Instruct-reason 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 dbands/Qwen2.5-Coder-7B-Instruct-reason 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 dbands/Qwen2.5-Coder-7B-Instruct-reason to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dbands/Qwen2.5-Coder-7B-Instruct-reason to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="dbands/Qwen2.5-Coder-7B-Instruct-reason",
    max_seq_length=2048,
)

Docker Model Runner
How to use dbands/Qwen2.5-Coder-7B-Instruct-reason with Docker Model Runner:
```
docker model run hf.co/dbands/Qwen2.5-Coder-7B-Instruct-reason
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

My Reasoning Model

System Prompt Format

Respond in the following format:

<reasoning>
...
</reasoning>
<answer>
...
</answer>

I fine-tuned the model using openai/gsm8k, and to ensure costs do not go insane, I used a single A100.


Enjoy, but please note that this model is experimental and I used it to define my pipeline.

I will be testing fine tuning larger more capable models.  I suspect they would add more value in the short term.


---

# Uploaded  model

- **Developed by:** dbands
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit

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)

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for dbands/Qwen2.5-Coder-7B-Instruct-reason

Quantizations

1 model