Instructions to use tensorblock/MAmmoTH2-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/MAmmoTH2-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/MAmmoTH2-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/MAmmoTH2-8B-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/MAmmoTH2-8B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/MAmmoTH2-8B-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/MAmmoTH2-8B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/MAmmoTH2-8B-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/MAmmoTH2-8B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/MAmmoTH2-8B-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/MAmmoTH2-8B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/MAmmoTH2-8B-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/MAmmoTH2-8B-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/MAmmoTH2-8B-GGUF with Ollama:
```
ollama run hf.co/tensorblock/MAmmoTH2-8B-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/MAmmoTH2-8B-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 tensorblock/MAmmoTH2-8B-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 tensorblock/MAmmoTH2-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/MAmmoTH2-8B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use tensorblock/MAmmoTH2-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/MAmmoTH2-8B-GGUF:Q2_K
```

Lemonade

How to use tensorblock/MAmmoTH2-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/MAmmoTH2-8B-GGUF:Q2_K

Run and chat with the model

lemonade run user.MAmmoTH2-8B-GGUF-Q2_K

List all available models

lemonade list

TIGER-Lab/MAmmoTH2-8B - GGUF

This repo contains GGUF format model files for TIGER-Lab/MAmmoTH2-8B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

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## Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{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
MAmmoTH2-8B-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
MAmmoTH2-8B-Q3_K_S.gguf	Q3_K_S	3.664 GB	very small, high quality loss
MAmmoTH2-8B-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
MAmmoTH2-8B-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
MAmmoTH2-8B-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
MAmmoTH2-8B-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
MAmmoTH2-8B-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
MAmmoTH2-8B-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
MAmmoTH2-8B-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
MAmmoTH2-8B-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
MAmmoTH2-8B-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
MAmmoTH2-8B-Q8_0.gguf	Q8_0	8.541 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/MAmmoTH2-8B-GGUF --include "MAmmoTH2-8B-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/MAmmoTH2-8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 29

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

Inference Providers NEW

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

Model tree for tensorblock/MAmmoTH2-8B-GGUF

Base model

TIGER-Lab/MAmmoTH2-8B

Quantized

(2)

this model

tensorblock
/

MAmmoTH2-8B-GGUF