Instructions to use musamrc/model_256_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musamrc/model_256_256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="musamrc/model_256_256") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("musamrc/model_256_256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use musamrc/model_256_256 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 musamrc/model_256_256:Q8_0 # Run inference directly in the terminal: llama cli -hf musamrc/model_256_256:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf musamrc/model_256_256:Q8_0 # Run inference directly in the terminal: llama cli -hf musamrc/model_256_256:Q8_0
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 musamrc/model_256_256:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf musamrc/model_256_256:Q8_0
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 musamrc/model_256_256:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf musamrc/model_256_256:Q8_0
Use Docker
docker model run hf.co/musamrc/model_256_256:Q8_0
- LM Studio
- Jan
- Ollama
How to use musamrc/model_256_256 with Ollama:
ollama run hf.co/musamrc/model_256_256:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use musamrc/model_256_256 with Docker Model Runner:
docker model run hf.co/musamrc/model_256_256:Q8_0
- Lemonade
How to use musamrc/model_256_256 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull musamrc/model_256_256:Q8_0
Run and chat with the model
lemonade run user.model_256_256-Q8_0
List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from musamrc/model_256_256: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/musamrc/model_256_256/resolve/main/tokenizer.json
- Command line
-
hf download hf://musamrc/model_256_256/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/musamrc/model_256_256/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- b710a8e3989751dc2c105f3dee3f93833cca2eb81982c411a24d393b0159a7c9
- Size of remote file:
- 17.5 MB
- SHA256:
- 7da53ca29fb16f6b2489482fc0bc6a394162cdab14d12764a1755ebc583fea79
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