Instructions to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit 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 Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit: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 Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit: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 Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M

Use Docker

docker model run hf.co/Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M

Ollama
How to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit with Ollama:
```
ollama run hf.co/Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit with Docker Model Runner:
```
docker model run hf.co/Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M
```

Lemonade

How to use Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit:Q4_K_M

Run and chat with the model

lemonade run user.Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mamba-Codestral-7B-Instruct_CODE_Python-Spanish_English_GGUF_4bit / unsloth.Q4_K_M.gguf

Agnuxo

(Trained with Unsloth)

7dd7af9 verified about 2 years ago

History Blame GGUF Editor new Delete

4.37 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:: f9cae00a647d680f4f551b9fcd7eeac91ce8ebab384279c5ea40fc26b4c8d23a
Size of remote file:: 4.37 GB
SHA256:: 537ce9425b6afc3fc9bcaa77707f073b02bb4572bdbb89bec96463f899de1615

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