Instructions to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit",
	filename="unsloth.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-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-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-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-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M

Use Docker

docker model run hf.co/Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-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-v0.1-instruct-python_coding_assistant-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-v0.1-instruct-python_coding_assistant-GGUF_4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M

Ollama
How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with Ollama:
```
ollama run hf.co/Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
```

Unsloth Studio new

How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit to start chatting

Docker Model Runner
How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with Docker Model Runner:
```
docker model run hf.co/Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M
```

Lemonade

How to use Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit:Q4_K_M

Run and chat with the model

lemonade run user.Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit-Q4_K_M

List all available models

lemonade list

Agnuxo commited on Aug 30, 2024

Commit

db9f483

verified ·

1 Parent(s): 450aae8

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -2

README.md CHANGED Viewed

@@ -1,7 +1,8 @@
 ---
-base_model: unsloth/mistral-7b-v0.3-bnb-4bit
 language:
 - en
 license: apache-2.0
 tags:
 - text-generation-inference
@@ -9,6 +10,14 @@ tags:
 - unsloth
 - mistral
 - gguf
 ---
 # Uploaded  model
@@ -19,4 +28,4 @@ tags:
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+base_model: Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit
 language:
 - en
+- es
 license: apache-2.0
 tags:
 - text-generation-inference
 - unsloth
 - mistral
 - gguf
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+- jtatman/python-code-dataset-500k
+- flytech/python-codes-25k
+- Vezora/Tested-143k-Python-Alpaca
+metrics:
+- code_eval
+library_name: adapter-transformers
 ---
 # Uploaded  model
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)