Instructions to use duyntnet/MythoMist-7b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use duyntnet/MythoMist-7b-imatrix-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="duyntnet/MythoMist-7b-imatrix-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/MythoMist-7b-imatrix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/MythoMist-7b-imatrix-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 duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use duyntnet/MythoMist-7b-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/MythoMist-7b-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/MythoMist-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/MythoMist-7b-imatrix-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "duyntnet/MythoMist-7b-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/MythoMist-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "duyntnet/MythoMist-7b-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/MythoMist-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use duyntnet/MythoMist-7b-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use duyntnet/MythoMist-7b-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/MythoMist-7b-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/MythoMist-7b-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MythoMist-7b-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Quantizations of https://huggingface.co/Gryphe/MythoMist-7b

Open source inference clients/UIs

Closed source inference clients/UIs

LM Studio
More will be added...

From original readme

MythoMist 7b is, as always, a highly experimental Mistral-based merge based on my latest algorithm, which actively benchmarks the model as it's being built in pursuit of a goal set by the user.

Addendum (2023-11-23): A more thorough investigation revealed a flaw in my original algorithm that has since been resolved. I've considered deleting this model as it did not follow its original objective completely but since there are plenty of folks enjoying it I'll be keeping it around. Keep a close eye on my MergeMonster repo for further developments and releases of merges produced by the Merge Monster.

The primary purpose for MythoMist was to reduce usage of the word anticipation, ministrations and other variations we've come to associate negatively with ChatGPT roleplaying data. This algorithm cannot outright ban these words, but instead strives to minimize the usage.

The script has now been made available on my Github. Warning - Plenty of VRAM is needed.

Final merge composition

After processing 12 models my algorithm ended up with the following (approximated) final composition:

Model	Contribution
Neural-chat-7b-v3-1	26%
Synatra-7B-v0.3-RP	22%
Airoboros-m-7b-3.1.2	10%
Toppy-M-7B	10%
Zephyr-7b-beta	7%
Nous-Capybara-7B-V1.9	5%
OpenHermes-2.5-Mistral-7B	5%
Dolphin-2.2.1-mistral-7b	4%
Noromaid-7b-v0.1.1	4%
SynthIA-7B-v1.3	3%
Mistral-7B-v0.1	2%
Openchat_3.5	2%

There is no real logic in how these models were divided throughout the merge - Small bits and pieces were taken from each and then mixed in with other models on a layer by layer basis, using a pattern similar to my MythoMax recipe in which underlying tensors are mixed in a criss-cross manner.

This new process only decides on the model's layers, not the singular lm_head and embed_tokens layers which influence much of the model's output. I ran a seperate script for that, picking the singular tensors that resulted in the longest responses, which settled on Toppy-M-7B.

Prompt Format

Due to the wide variation in prompt formats used in this merge I (for now) recommend using Alpaca as the prompt template for compatibility reasons:

### Instruction:
Your instruction or question here.

### Response:

Downloads last month: 522

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit