Transformers
PyTorch
Safetensors
GGUF
English
llama
text-generation-inference
unsloth
trl
sft
conversational
Instructions to use Ramikan-BR/tinyllama-coder-py-4bit-v7 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-v7 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ramikan-BR/tinyllama-coder-py-4bit-v7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Ramikan-BR/tinyllama-coder-py-4bit-v7 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-v7:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v7: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-v7:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v7: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-v7:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v7: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-v7:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ramikan-BR/tinyllama-coder-py-4bit-v7:Q4_K_M
Use Docker
docker model run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v7:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Ramikan-BR/tinyllama-coder-py-4bit-v7 with Ollama:
ollama run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v7:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Ramikan-BR/tinyllama-coder-py-4bit-v7 with Docker Model Runner:
docker model run hf.co/Ramikan-BR/tinyllama-coder-py-4bit-v7:Q4_K_M
- Lemonade
How to use Ramikan-BR/tinyllama-coder-py-4bit-v7 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ramikan-BR/tinyllama-coder-py-4bit-v7:Q4_K_M
Run and chat with the model
lemonade run user.tinyllama-coder-py-4bit-v7-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download pytorch_model.bin from Ramikan-BR/tinyllama-coder-py-4bit-v7: direct link, hf CLI and curl.
- Browser
- Download file 2.2 GB
-
https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v7/resolve/b1bb32b9de2ae51ad0a7d4ada45145246bd13985/pytorch_model.bin
- Command line
-
hf download hf://Ramikan-BR/tinyllama-coder-py-4bit-v7@b1bb32b9de2ae51ad0a7d4ada45145246bd13985/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v7/resolve/b1bb32b9de2ae51ad0a7d4ada45145246bd13985/pytorch_model.bin
2.2 GB
- Xet hash:
- 0e9ae28c5e417ac7352cd9d87f092236b6d53badd462e5887d185e05448ad641
- Size of remote file:
- 2.2 GB
- SHA256:
- 2e987ea8d781db92ae91347e9c6607fada7baa89b3cf727f9a1f152933982e2d
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