Instructions to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/mathstral-7B-v0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MaziyarPanahi/mathstral-7B-v0.1-GGUF", dtype="auto")

llama-cpp-python

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MaziyarPanahi/mathstral-7B-v0.1-GGUF",
	filename="mathstral-7B-v0.1.IQ1_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/mathstral-7B-v0.1-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 MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/mathstral-7B-v0.1-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 MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/mathstral-7B-v0.1-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 MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/mathstral-7B-v0.1-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 MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/mathstral-7B-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/mathstral-7B-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M

SGLang

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MaziyarPanahi/mathstral-7B-v0.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/mathstral-7B-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MaziyarPanahi/mathstral-7B-v0.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/mathstral-7B-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF 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 MaziyarPanahi/mathstral-7B-v0.1-GGUF 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 MaziyarPanahi/mathstral-7B-v0.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MaziyarPanahi/mathstral-7B-v0.1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/mathstral-7B-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/mathstral-7B-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mathstral-7B-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

MaziyarPanahi commited on Jul 16, 2024

Commit

d86040a

verified ·

1 Parent(s): 9d69c68

e266546623f45eb0851454548503ced1edac6b425be9ee7151cbde1c7713e560

Browse files

Files changed (4) hide show

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README.md +46 -3
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README.md CHANGED Viewed

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----

+---
+tags:
+- quantized
+- 2-bit
+- 3-bit
+- 4-bit
+- 5-bit
+- 6-bit
+- 8-bit
+- GGUF
+- text-generation
+- text-generation
+model_name: mathstral-7B-v0.1-GGUF
+base_model: mistralai/mathstral-7B-v0.1
+inference: false
+model_creator: mistralai
+pipeline_tag: text-generation
+quantized_by: MaziyarPanahi
+---
+# [MaziyarPanahi/mathstral-7B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF)
+- Model creator: [mistralai](https://huggingface.co/mistralai)
+- Original model: [mistralai/mathstral-7B-v0.1](https://huggingface.co/mistralai/mathstral-7B-v0.1)
+## Description
+[MaziyarPanahi/mathstral-7B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mathstral-7B-v0.1-GGUF) contains GGUF format model files for [mistralai/mathstral-7B-v0.1](https://huggingface.co/mistralai/mathstral-7B-v0.1).
+### About GGUF
+GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
+Here is an incomplete list of clients and libraries that are known to support GGUF:
+* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
+* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
+* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
+* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
+* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
+## Special thanks
+🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

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