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

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

tinyllama-coder-py-4bit-v7 / tinyllama-coder-py-4bit-v7-unsloth.Q4_K_M.gguf

Ramikan-BR

(Trained with Unsloth)

05b6499 verified over 2 years ago

History Blame

668 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 1aa6242ab66f2463733d958d2d78f3b94597aad8bc6fa380dae489ba858a5436
Size of remote file:: 668 MB
SHA256:: f0c6b65d1a7cbf703eaa3937760fa6af61ee021556bf1b1bee572040367dd7a0

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.