Instructions to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF",
	filename="deepthought-8b-llama-v0.01-alpha.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

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 QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

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 QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

Ollama
How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-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 QuantFactory/deepthought-8b-llama-v0.01-alpha-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 QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF to start chatting

Pi new

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

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": "QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

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 QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.deepthought-8b-llama-v0.01-alpha-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/deepthought-8b-llama-v0.01-alpha-GGUF

This is quantized version of ruliad/deepthought-8b-llama-v0.01-alpha created using llama.cpp

Original Model Card

Deepthought-8B

Deepthought-8B is a small and capable reasoning model built on LLaMA-3.1 8B, designed to make AI reasoning more transparent and controllable. Despite its relatively small size, it achieves sophisticated reasoning capabilities that rival much larger models.

Model Description

Deepthought-8B is designed with a unique approach to problem-solving, breaking down its thinking into clear, distinct, documented steps. The model outputs its reasoning process in a structured JSON format, making it easier to understand and validate its decision-making process.

Key Features

Transparent Reasoning: Step-by-step documentation of the thought process
Programmable Approach: Customizable reasoning patterns without model retraining
Test-time Compute Scaling: Flexible reasoning depth based on task complexity
Efficient Scale: Runs on 16GB+ VRAM
Structured Output: JSON-formatted reasoning chains for easy integration

Try out Deepthought-8B on our Ruliad interface: https://chat.ruliad.co

Technical Requirements

Python 3.6+
PyTorch
Transformers library
16GB+ VRAM
Optional: Flash Attention 2 for improved performance

Installation

pip install torch transformers
# Optional: Install Flash Attention 2 for better performance
pip install flash-attn

Usage

First, set your HuggingFace token as an environment variable:

export HF_TOKEN=your_token_here
export HF_HUB_ENABLE_HF_TRANSFER=1

Use the model in your Python code:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Initialize the model
model_name = "ruliad/deepthought-8b-llama-v0.01-alpha"
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    add_bos_token=False,
    trust_remote_code=True,
    padding="left",
    torch_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="flash_attention_2",  # Use "eager" (or omit) if flash_attn is not installed
    use_cache=True,
    trust_remote_code=True,
)

Run the provided example script:

python deepthought_inference.py

Example Output

The model provides structured reasoning in JSON format:

{
  "step": 1,
  "type": "problem_understanding",
  "thought": "Understanding the user's objective for the task."
}

Each reasoning chain includes multiple steps:

Problem understanding
Data gathering
Analysis
Calculation (when applicable)
Verification
Conclusion drawing
Implementation

Performance

Deepthought-8B demonstrates strong performance across various benchmarks:

Step-by-step problem-solving
Coding and mathematical tasks
Instruction following with transparent reasoning
Scalable performance with test-time compute

Limitations

Current known limitations include:

Complex mathematical reasoning
Long-context processing
Edge case handling

License

The model is available under a commercial license for enterprise use.

Citation

If you use this model in your research, please cite:

@misc{Deepthought2024,
  author = {Ruliad},
  title = {Deepthought-8B: A Small and Capable Reasoning Model},
  year = {2024},
  publisher = {Ruliad}
}

Support

For questions and feedback:

Twitter: @ruliad_ai
Email: team@ruliad.co

Downloads last month: 69

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit