Instructions to use DavidOKB/MathThink-Qwen-3.5-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidOKB/MathThink-Qwen-3.5-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DavidOKB/MathThink-Qwen-3.5-4B",
	filename="MathThink-Qwen-3.5-4B_alpha16/MathThink-Qwen-3.5-4B_alpha16-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DavidOKB/MathThink-Qwen-3.5-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

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 DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

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 DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Use Docker

docker model run hf.co/DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

LM Studio
Jan
Ollama
How to use DavidOKB/MathThink-Qwen-3.5-4B with Ollama:
```
ollama run hf.co/DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
```

Unsloth Studio

How to use DavidOKB/MathThink-Qwen-3.5-4B 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 DavidOKB/MathThink-Qwen-3.5-4B 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 DavidOKB/MathThink-Qwen-3.5-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DavidOKB/MathThink-Qwen-3.5-4B to start chatting

How to use DavidOKB/MathThink-Qwen-3.5-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DavidOKB/MathThink-Qwen-3.5-4B:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DavidOKB/MathThink-Qwen-3.5-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use DavidOKB/MathThink-Qwen-3.5-4B with Docker Model Runner:
```
docker model run hf.co/DavidOKB/MathThink-Qwen-3.5-4B:Q8_0
```

Lemonade

How to use DavidOKB/MathThink-Qwen-3.5-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidOKB/MathThink-Qwen-3.5-4B:Q8_0

Run and chat with the model

lemonade run user.MathThink-Qwen-3.5-4B-Q8_0

List all available models

lemonade list

MathThink-Qwen-3.5-4B

File size: 1,542 Bytes

---
license: apache-2.0
tags:
- math
- reasoning
- qwen
- llama-cpp
- gguf
- lora
- chain-of-thought
datasets:
- nvidia/Nemotron-SFT-Math-v3
base_model:
- Qwen/Qwen3.5-4B
---

# Qwen3.5-4B Math Fine-Tuned (Nemotron-SFT-Math-v3)

This model is a fine-tuned version of `Qwen3.5-4B`, explicitly optimized for complex mathematical reasoning and Chain-of-Thought (CoT) problem solving. It was fine-tuned using the `Nemotron-Math-v3` dataset with Parameter-Efficient Fine-Tuning (PEFT/LoRA).

## Model Details

- **Base Model**: `Qwen/Qwen3.5-4B`
- **Fine-Tuning Dataset**: `nvidia/Nemotron-SFT-Math-v3`
- **Methodology**: LoRA (Rank = 64, Alpha = 32 or Alpha = 16). The `lora_alpha` scaling is specifically tuned to prevent catastrophic forgetting, ensuring the model retains conversational abilities while significantly enhancing mathematical logic.
- **Quantization**: Safetensor format (`F16`) and GGUF formats (`Q8_0`)

## Recommended Generation Parameters

Because this model leverages extensive Chain-of-Thought reasoning to solve math problems, the following generation parameters are highly recommended for the best performance:

```json
{
  "temperature": 1.0,
  "top_p": 0.95,
  "repetition_penalty": 1.1
}
```

*Note: A `repetition_penalty` of `1.1` is crucial to prevent the base model from occasionally falling into infinite generation loops on extremely long context windows.*

## Use Cases

- Resolving complex math word problems (GSM8K).
- Higher-level mathematical reasoning (MATH, AIME).
- Step-by-step logic tracking and proofs.