Instructions to use tensorblock/deepthought-8b-llama-v0.01-alpha-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 tensorblock/deepthought-8b-llama-v0.01-alpha-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 tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

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 tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

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 tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/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 "tensorblock/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": "tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Ollama
How to use tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF with Ollama:
```
ollama run hf.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
```
Unsloth Desktop

How to use tensorblock/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 serve -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Run and chat with the model

lemonade run user.deepthought-8b-llama-v0.01-alpha-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use tensorblock/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 serve -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

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 tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K

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 "tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF:Q2_K" \
  --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"

deepthought-8b-llama-v0.01-alpha-GGUF / README.md

morriszms

Upload folder using huggingface_hub

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	---
	license: llama3.1
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- TensorBlock
	- GGUF
	base_model: ruliad/deepthought-8b-llama-v0.01-alpha
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
	<div style="display: flex; justify-content: space-between; width: 100%;">
	<div style="display: flex; flex-direction: column; align-items: flex-start;">
	<p style="margin-top: 0.5em; margin-bottom: 0em;">
	Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
	</p>
	</div>
	</div>

	## ruliad/deepthought-8b-llama-v0.01-alpha - GGUF

	This repo contains GGUF format model files for [ruliad/deepthought-8b-llama-v0.01-alpha](https://huggingface.co/ruliad/deepthought-8b-llama-v0.01-alpha).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

	<div style="text-align: left; margin: 20px 0;">
	<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
	Run them on the TensorBlock client using your local machine ↗
	</a>
	</div>

	## Prompt template

	```
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	Cutting Knowledge Date: December 2023
	Today Date: 26 Jul 2024

	{system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [deepthought-8b-llama-v0.01-alpha-Q2_K.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q2_K.gguf) \| Q2_K \| 3.179 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [deepthought-8b-llama-v0.01-alpha-Q3_K_S.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q3_K_S.gguf) \| Q3_K_S \| 3.665 GB \| very small, high quality loss \|
	\| [deepthought-8b-llama-v0.01-alpha-Q3_K_M.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q3_K_M.gguf) \| Q3_K_M \| 4.019 GB \| very small, high quality loss \|
	\| [deepthought-8b-llama-v0.01-alpha-Q3_K_L.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q3_K_L.gguf) \| Q3_K_L \| 4.322 GB \| small, substantial quality loss \|
	\| [deepthought-8b-llama-v0.01-alpha-Q4_0.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q4_0.gguf) \| Q4_0 \| 4.661 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [deepthought-8b-llama-v0.01-alpha-Q4_K_S.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q4_K_S.gguf) \| Q4_K_S \| 4.693 GB \| small, greater quality loss \|
	\| [deepthought-8b-llama-v0.01-alpha-Q4_K_M.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q4_K_M.gguf) \| Q4_K_M \| 4.921 GB \| medium, balanced quality - recommended \|
	\| [deepthought-8b-llama-v0.01-alpha-Q5_0.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q5_0.gguf) \| Q5_0 \| 5.599 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [deepthought-8b-llama-v0.01-alpha-Q5_K_S.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q5_K_S.gguf) \| Q5_K_S \| 5.599 GB \| large, low quality loss - recommended \|
	\| [deepthought-8b-llama-v0.01-alpha-Q5_K_M.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q5_K_M.gguf) \| Q5_K_M \| 5.733 GB \| large, very low quality loss - recommended \|
	\| [deepthought-8b-llama-v0.01-alpha-Q6_K.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q6_K.gguf) \| Q6_K \| 6.596 GB \| very large, extremely low quality loss \|
	\| [deepthought-8b-llama-v0.01-alpha-Q8_0.gguf](https://huggingface.co/tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF/blob/main/deepthought-8b-llama-v0.01-alpha-Q8_0.gguf) \| Q8_0 \| 8.541 GB \| very large, extremely low quality loss - not recommended \|


	## Downloading instruction

	### Command line

	Firstly, install Huggingface Client

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	huggingface-cli download tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF --include "deepthought-8b-llama-v0.01-alpha-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/deepthought-8b-llama-v0.01-alpha-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```