Instructions to use Gryphe/MythoMist-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gryphe/MythoMist-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gryphe/MythoMist-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gryphe/MythoMist-7b") model = AutoModelForCausalLM.from_pretrained("Gryphe/MythoMist-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Gryphe/MythoMist-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gryphe/MythoMist-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gryphe/MythoMist-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Gryphe/MythoMist-7b
- SGLang
How to use Gryphe/MythoMist-7b 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 "Gryphe/MythoMist-7b" \ --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": "Gryphe/MythoMist-7b", "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 "Gryphe/MythoMist-7b" \ --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": "Gryphe/MythoMist-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Gryphe/MythoMist-7b with Docker Model Runner:
docker model run hf.co/Gryphe/MythoMist-7b
Update README.md
Browse files
README.md
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@@ -9,7 +9,7 @@ MythoMist 7b is, as always, a highly experimental Mistral-based merge based on m
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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.
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Quantized models are available from TheBloke: [GGUF](https://huggingface.co/TheBloke/MythoMist-7B-GGUF) - [GPTQ](https://huggingface.co/TheBloke/MythoMist-7B-GPTQ) - [GPTQ](https://huggingface.co/TheBloke/MythoMist-7B-AWQ) (You're the best!)
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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.
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[The script has now been made available on my Github. Warning - Plenty of VRAM is needed.](https://github.com/Gryphe/MergeMonster/)
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Quantized models are available from TheBloke: [GGUF](https://huggingface.co/TheBloke/MythoMist-7B-GGUF) - [GPTQ](https://huggingface.co/TheBloke/MythoMist-7B-GPTQ) - [GPTQ](https://huggingface.co/TheBloke/MythoMist-7B-AWQ) (You're the best!)
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