Instructions to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-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 v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_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 v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_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 v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF with Ollama:
```
ollama run hf.co/v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
```

Unsloth Studio

How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-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 v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-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 v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF to start chatting

Docker Model Runner
How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K
```

Lemonade

How to use v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull v000000/Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.Typhon-Mixtral-v1-imatrix-v2.Q6_K-GGUF-Q6_K

List all available models

lemonade list

Atomic Chat

Contains: Q6_K GGUF (imatrix)
Can fit in 16GB VRAM+32GB RAM, n_ctx 8k, 12 layers, blas 1024, reading speed generation

Mixtral 8x7B merge by Sao10k

(!!! ARCHIVE !!!)

Calibrated imatrix data (V2 - 287kb) randomized bartowski, kalomeze groups, ERP/RP snippets, working gpt4 code, toxic qa, human messaging, randomized posts, story, novels

Typhon - A Custom Experimental Mixtral Merge

An experimental Merge I tried for fun. Honestly did not expect it to work for Mixtral at all considering how its an MoE and the gates and all would be fucked by this custom merge.

From my testing it was able to handle SFW <--> NSFW scenarios fine, handle 1st and 3rd person roleplays fine, and seemed fairly smart.

It did pretty well for non NSFW tasks so that's a win.

Due to the nature of the merge, and Mixtral itself, it is sensitive to Prompts, does follow them well. Sampler settings are fine. i stuck with universal-light and was okay at up to 16k context during testing.

Recipe Below:

base_model: mistralai/Mixtral-8x7B-v0.1
models:
  - model: mistralai/Mixtral-8x7B-v0.1
    # no parameters necessary for base model
  - model: smelborp/MixtralOrochi8x7B
    parameters:
      weight: 0.30
      density: 0.47
  - model:  notstoic/Nous-Hermes-2-Mixtruct-v0.1-8x7B-DPO-DARE_TIES
    parameters:
      weight: 0.31
      density: 0.56
  - model: Sao10K/Solstice-Mixtral-v1
    parameters: 
      weight: 0.36
      density: 0.64
  - model: Sao10K/Frostwind-Mixtral-v1
    parameters:
      weight: 0.22
      density: 0.44
  - model: KoboldAI/Mixtral-8x7B-Holodeck-v1
    parameters:
      weight: 0.21
      density: 0.36
merge_method: dare_ties
dtype: bfloat16

Downloads last month: 70

GGUF

Model size

47B params

Architecture

llama

Hardware compatibility

6-bit

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mistralai/Mixtral-8x7B-v0.1

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