Instructions to use BlueNipples/Apocrypha-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BlueNipples/Apocrypha-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BlueNipples/Apocrypha-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BlueNipples/Apocrypha-7b") model = AutoModelForCausalLM.from_pretrained("BlueNipples/Apocrypha-7b") - llama-cpp-python
How to use BlueNipples/Apocrypha-7b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BlueNipples/Apocrypha-7b", filename="GGUF/Apocrypha-7B-Q5_k.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BlueNipples/Apocrypha-7b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX # Run inference directly in the terminal: llama-cli -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX # Run inference directly in the terminal: llama-cli -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
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 BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX # Run inference directly in the terminal: ./llama-cli -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
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 BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX # Run inference directly in the terminal: ./build/bin/llama-cli -hf BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
Use Docker
docker model run hf.co/BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
- LM Studio
- Jan
- vLLM
How to use BlueNipples/Apocrypha-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BlueNipples/Apocrypha-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlueNipples/Apocrypha-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
- SGLang
How to use BlueNipples/Apocrypha-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 "BlueNipples/Apocrypha-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": "BlueNipples/Apocrypha-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 "BlueNipples/Apocrypha-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": "BlueNipples/Apocrypha-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use BlueNipples/Apocrypha-7b with Ollama:
ollama run hf.co/BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
- Unsloth Studio
How to use BlueNipples/Apocrypha-7b 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 BlueNipples/Apocrypha-7b 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 BlueNipples/Apocrypha-7b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BlueNipples/Apocrypha-7b to start chatting
- Docker Model Runner
How to use BlueNipples/Apocrypha-7b with Docker Model Runner:
docker model run hf.co/BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
- Lemonade
How to use BlueNipples/Apocrypha-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BlueNipples/Apocrypha-7b:IQ2_XXS_IMATRIX
Run and chat with the model
lemonade run user.Apocrypha-7b-IQ2_XXS_IMATRIX
List all available models
lemonade list
Update README.md
Browse files
README.md
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tags:
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# Apocrypha-7b
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##
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### Merge Method
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This
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* ./mental-mistral-7b
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* ./Fett-uccine-7B
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* ./Hermes-Trismegistus-7B
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### Configuration
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base_model: ./Mistral-7B-v0.1
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dtype: bfloat16
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```
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- conversation
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### Design
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This mistral 7b model is a task arithmetic merge of Epiculous/Fett-uccine-7B (theory of mind and gnosis datasets), GRMenon/mental-mistral-7b-instruct-autotrain (mental health counseling conversations dataset), and teknium/Hermes-Trismegistus-Mistral-7B (open-hermes + occult datasets)
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The design intention is to create a pseudo-philosophical, pseudo-spiritual, pseudo counseling chatbob model for sounding ideas off. Like a mirror really. This obviously does not constitute medical advice, and if you are in need seek professional help. The name Apocrypha-7B comes from the fact that it's fake - this isn't a guide or a guru. It's at best, if the model works, a sounding board. But I think such things might still be helpful for organising ones own thoughts.
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I will throw a GGUF or two inside a subfolder here.
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### Configuration
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base_model: ./Mistral-7B-v0.1
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dtype: bfloat16
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```
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Resources used:
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https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B
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https://huggingface.co/GRMenon/mental-mistral-7b-instruct-autotrain
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https://huggingface.co/Epiculous/Fett-uccine-7B/tree/main
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https://github.com/cg123/mergekit/tree/main
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