Instructions to use RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf 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 RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf: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 RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf: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 RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/meta-llama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M

Run and chat with the model

lemonade run user.meta-llama_-_CodeLlama-70b-Python-hf-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

meta-llama_-_CodeLlama-70b-Python-hf-gguf / CodeLlama-70b-Python-hf.IQ3_XS.gguf

RichardErkhov

uploaded model

7cba608 verified about 2 years ago

History Blame

28.3 GB

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:: 3c06bec12c8cbb9cd3b699601c0bbca900762e9b24deda18d366edb1883bbe76
Size of remote file:: 28.3 GB
SHA256:: 8909d81c07a052ae24cb73c3f3b865d8567c4b36a42ca03daf3e2b7913cf5611

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