How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
# Run inference directly in the terminal:
llama-cli -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
# Run inference directly in the terminal:
llama-cli -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
# Run inference directly in the terminal:
./llama-cli -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
Use Docker
docker model run hf.co/dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf:
Quick Links

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
146
GGUF
Model size
8B params
Architecture
qwen2
Hardware compatibility
Log In to add your hardware

4-bit

5-bit

8-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Dataset used to train dbands/Qwen2.5-Coder-7B-Instruct-reason-gguf