Instructions to use shibatch/stories-converted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibatch/stories-converted with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/stories-converted", dtype="auto") - llama-cpp-python
How to use shibatch/stories-converted with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="shibatch/stories-converted", filename="stories15M.BF16.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 shibatch/stories-converted with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shibatch/stories-converted:BF16 # Run inference directly in the terminal: llama-cli -hf shibatch/stories-converted:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shibatch/stories-converted:BF16 # Run inference directly in the terminal: llama-cli -hf shibatch/stories-converted:BF16
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 shibatch/stories-converted:BF16 # Run inference directly in the terminal: ./llama-cli -hf shibatch/stories-converted:BF16
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 shibatch/stories-converted:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf shibatch/stories-converted:BF16
Use Docker
docker model run hf.co/shibatch/stories-converted:BF16
- LM Studio
- Jan
- Ollama
How to use shibatch/stories-converted with Ollama:
ollama run hf.co/shibatch/stories-converted:BF16
- Unsloth Studio
How to use shibatch/stories-converted 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 shibatch/stories-converted 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 shibatch/stories-converted to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shibatch/stories-converted to start chatting
- Docker Model Runner
How to use shibatch/stories-converted with Docker Model Runner:
docker model run hf.co/shibatch/stories-converted:BF16
- Lemonade
How to use shibatch/stories-converted with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shibatch/stories-converted:BF16
Run and chat with the model
lemonade run user.stories-converted-BF16
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +20 -0
- stories15M.BF16.gguf +3 -0
- stories15M.F16.gguf +3 -0
- stories15M.F32.gguf +3 -0
- stories15M.IQ3_S.gguf +3 -0
- stories15M.IQ3_XXS.gguf +3 -0
- stories15M.IQ4_XS.gguf +3 -0
- stories15M.Q2_K.gguf +3 -0
- stories15M.Q3_K.gguf +3 -0
- stories15M.Q4_0.gguf +3 -0
- stories15M.Q4_1.gguf +3 -0
- stories15M.Q4_K.gguf +3 -0
- stories15M.Q5_0.gguf +3 -0
- stories15M.Q5_1.gguf +3 -0
- stories15M.Q5_K.gguf +3 -0
- stories15M.Q6_K.gguf +3 -0
- stories15M.Q8_0.gguf +3 -0
- stories15M.TQ1_0.gguf +3 -0
- stories15M.TQ2_0.gguf +3 -0
- stories260K.F16.gguf +3 -0
- stories260K.F32.gguf +3 -0
.gitattributes
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