Transformers
Safetensors
GGUF
English
llama
text-generation-inference
unsloth
trl
sft
conversational
Instructions to use Ramikan-BR/tinyllama-coder-py-4bit-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ramikan-BR/tinyllama-coder-py-4bit-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ramikan-BR/tinyllama-coder-py-4bit-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Ramikan-BR/tinyllama-coder-py-4bit-v2 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 Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
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 Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
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 Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
Use Docker
docker model run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Ramikan-BR/tinyllama-coder-py-4bit-v2 with Ollama:
ollama run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Ramikan-BR/tinyllama-coder-py-4bit-v2 with Docker Model Runner:
docker model run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
- Lemonade
How to use Ramikan-BR/tinyllama-coder-py-4bit-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ramikan-BR/tinyllama-coder-py-4bit-v2:Q4_K_M
Run and chat with the model
lemonade run user.tinyllama-coder-py-4bit-v2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download model.safetensors from Ramikan-BR/tinyllama-coder-py-4bit-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.2 GB
-
https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v2/resolve/main/model.safetensors
- Command line
-
hf download hf://Ramikan-BR/tinyllama-coder-py-4bit-v2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v2/resolve/main/model.safetensors
2.2 GB
- Xet hash:
- 16017465164500e825740b60002650b7acb86f471f0c763776896ef38493452a
- Size of remote file:
- 2.2 GB
- SHA256:
- 4587d50302a05286f9d1087902c9045f28dd608834a18606b6eb3ae6419dcb1e
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