Instructions to use benjamin920101/JT-Math-8B-Thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use benjamin920101/JT-Math-8B-Thinking-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

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 benjamin920101/JT-Math-8B-Thinking-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

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 benjamin920101/JT-Math-8B-Thinking-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

Use Docker

docker model run hf.co/benjamin920101/JT-Math-8B-Thinking-GGUF:F16

LM Studio
Jan
Ollama
How to use benjamin920101/JT-Math-8B-Thinking-GGUF with Ollama:
```
ollama run hf.co/benjamin920101/JT-Math-8B-Thinking-GGUF:F16
```

Unsloth Studio

How to use benjamin920101/JT-Math-8B-Thinking-GGUF 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 benjamin920101/JT-Math-8B-Thinking-GGUF 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 benjamin920101/JT-Math-8B-Thinking-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for benjamin920101/JT-Math-8B-Thinking-GGUF to start chatting

How to use benjamin920101/JT-Math-8B-Thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

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": "benjamin920101/JT-Math-8B-Thinking-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use benjamin920101/JT-Math-8B-Thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/benjamin920101/JT-Math-8B-Thinking-GGUF:F16
```

Lemonade

How to use benjamin920101/JT-Math-8B-Thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull benjamin920101/JT-Math-8B-Thinking-GGUF:F16

Run and chat with the model

lemonade run user.JT-Math-8B-Thinking-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use benjamin920101/JT-Math-8B-Thinking-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

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 benjamin920101/JT-Math-8B-Thinking-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use benjamin920101/JT-Math-8B-Thinking-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf benjamin920101/JT-Math-8B-Thinking-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "benjamin920101/JT-Math-8B-Thinking-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

JT-Math-8B-Thinking-GGUF / README.md

benjamin920101

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	# JT-LM/JT-Math-8B-Thinking-GGUF

	This repository contains GGUF format model files converted from [JT-LM/JT-Math-8B-Thinking](https://huggingface.co/JT-LM/JT-Math-8B-Thinking), optimized for `llama.cpp` and other GGUF-compatible inference clients (such as LM Studio, Ollama, AnythingLLM, etc.).

	## Model Overview
	JT-Math-8B-Thinking is an 8-billion parameter open-source Large Language Model designed specifically for advanced mathematical reasoning and complex problem-solving. Fine-tuned on high-quality bilingual (Chinese and English) datasets, the model features strong long-context processing capabilities and powerful Chain-of-Thought (CoT) reasoning.

	- Key Features:
	- Long Context Support: Natively supports up to a 32,768 (32K) context window.
	- Deep Reasoning: Optimized via multi-stage Reinforcement Learning (RL) and curriculum learning, making it exceptionally good at generating deep reasoning paths to solve competition-level math problems.
	- Bilingual Optimization: Delivers top-tier mathematical derivation performance in both Chinese and English environments.

	---

	## File List & Quantization Options

	This repository offers two high-precision versions, ideal for scenarios that demand ultimate reasoning quality and have sufficient hardware resources:

	\| File Name \| Type \| File Size \| Recommended RAM/VRAM \| Description \|
	\| :--- \| :--- \| :---: \| :---: \| :--- \|
	\| `JT-Math-8B-Thinking-Q8_0.gguf` \| Q8_0 Quantization \| ~8.5 GB \| >= 12 GB \| Recommended Choice. Almost lossless 8-bit quantization that perfectly balances inference speed and model performance. Suitable for most modern CPUs and GPUs. \|
	\| `JT-Math-8B-Thinking-F16.gguf` \| F16 Native \| ~16.1 GB \| >= 24 GB \| Lossless Version. Retains the original Float16 precision. Ideal for resource-rich environments (e.g., 24GB VRAM GPUs) where any quantization loss is unacceptable. \|

	# JT-LM/JT-Math-8B-Thinking-GGUF

	This repository contains GGUF format model files converted from [JT-LM/JT-Math-8B-Thinking](https://huggingface.co/JT-LM/JT-Math-8B-Thinking), optimized for `llama.cpp` and other GGUF-compatible inference clients (such as LM Studio, Ollama, AnythingLLM, etc.).

	## Model Overview
	JT-Math-8B-Thinking is an 8-billion parameter open-source Large Language Model designed specifically for advanced mathematical reasoning and complex problem-solving. Fine-tuned on high-quality bilingual (Chinese and English) datasets, the model features strong long-context processing capabilities and powerful Chain-of-Thought (CoT) reasoning.

	- Key Features:
	- Long Context Support: Natively supports up to a 32,768 (32K) context window.
	- Deep Reasoning: Optimized via multi-stage Reinforcement Learning (RL) and curriculum learning, making it exceptionally good at generating deep reasoning paths to solve competition-level math problems.
	- Bilingual Optimization: Delivers top-tier mathematical derivation performance in both Chinese and English environments.

	---

	## File List & Quantization Options

	This repository offers two high-precision versions, ideal for scenarios that demand ultimate reasoning quality and have sufficient hardware resources:

	\| File Name \| Type \| File Size \| Recommended RAM/VRAM \| Description \|
	\| :--- \| :--- \| :---: \| :---: \| :--- \|
	\| `JT-Math-8B-Thinking-Q8_0.gguf` \| Q8_0 Quantization \| ~8.5 GB \| >= 12 GB \| Recommended Choice. Almost lossless 8-bit quantization that perfectly balances inference speed and model performance. Suitable for most modern CPUs and GPUs. \|
	\| `JT-Math-8B-Thinking-F16.gguf` \| F16 Native \| ~16.1 GB \| >= 24 GB \| Lossless Version. Retains the original Float16 precision. Ideal for resource-rich environments (e.g., 24GB VRAM GPUs) where any quantization loss is unacceptable. \|