Instructions to use ramcargpt/Batt_Gemma7B_38K_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramcargpt/Batt_Gemma7B_38K_128 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ramcargpt/Batt_Gemma7B_38K_128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ramcargpt/Batt_Gemma7B_38K_128 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 ramcargpt/Batt_Gemma7B_38K_128:Q8_0 # Run inference directly in the terminal: llama cli -hf ramcargpt/Batt_Gemma7B_38K_128:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ramcargpt/Batt_Gemma7B_38K_128:Q8_0 # Run inference directly in the terminal: llama cli -hf ramcargpt/Batt_Gemma7B_38K_128: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 ramcargpt/Batt_Gemma7B_38K_128:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ramcargpt/Batt_Gemma7B_38K_128: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 ramcargpt/Batt_Gemma7B_38K_128:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ramcargpt/Batt_Gemma7B_38K_128:Q8_0
Use Docker
docker model run hf.co/ramcargpt/Batt_Gemma7B_38K_128:Q8_0
- LM Studio
- Jan
- Ollama
How to use ramcargpt/Batt_Gemma7B_38K_128 with Ollama:
ollama run hf.co/ramcargpt/Batt_Gemma7B_38K_128:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use ramcargpt/Batt_Gemma7B_38K_128 with Docker Model Runner:
docker model run hf.co/ramcargpt/Batt_Gemma7B_38K_128:Q8_0
- Lemonade
How to use ramcargpt/Batt_Gemma7B_38K_128 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ramcargpt/Batt_Gemma7B_38K_128:Q8_0
Run and chat with the model
lemonade run user.Batt_Gemma7B_38K_128-Q8_0
List all available models
lemonade list
- Atomic Chat
Download unsloth.Q8_0.gguf from ramcargpt/Batt_Gemma7B_38K_128: direct link, hf CLI and curl.
- Browser
- Download file 9.08 GB
-
https://huggingface.co/ramcargpt/Batt_Gemma7B_38K_128/resolve/main/unsloth.Q8_0.gguf
- Command line
-
hf download hf://ramcargpt/Batt_Gemma7B_38K_128/unsloth.Q8_0.gguf
-
curl -L -o unsloth.Q8_0.gguf https://huggingface.co/ramcargpt/Batt_Gemma7B_38K_128/resolve/main/unsloth.Q8_0.gguf
9.08 GB
- Xet hash:
- b7caba5fe3ca490b5333ad05f4532122c514b80b6256b99f1375191210a12c56
- Size of remote file:
- 9.08 GB
- SHA256:
- 82f36775b4436a81ec26b174b10c68c39272572037a6147b20b81e756ffbadd5
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