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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koesn/Mistral-7B-v0.1-GGUF",
	filename="mistral-7b-instruct-v0.2.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

docker model run hf.co/koesn/Mistral-7B-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use koesn/Mistral-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 "koesn/Mistral-7B-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koesn/Mistral-7B-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/koesn/Mistral-7B-v0.1-GGUF:Q4_K_M

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

Unsloth Studio

How to use koesn/Mistral-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 koesn/Mistral-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 koesn/Mistral-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 koesn/Mistral-7B-v0.1-GGUF to start chatting

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

Lemonade

How to use koesn/Mistral-7B-v0.1-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Mistral-7B-v0.1

Description

This repo contains GGUF format model files for Mistral-7B-v0.1.

Files Provided

Name	Quant	Bits	File Size	Remark
mistral-7b-v0.1.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
mistral-7b-v0.1.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
mistral-7b-v0.1.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
mistral-7b-v0.1.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
mistral-7b-v0.1.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
mistral-7b-v0.1.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
mistral-7b-v0.1.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
mistral-7b-v0.1.Q8_0.gguf	Q8_0	8	7.70 GB	6.70G, +0.0004 ppl

Parameters

path	type	architecture	rope_theta	sliding_win	max_pos_embed
mistralai/Mistral-7B-v0.1	mistral	MistralForCausalLM	10000.0	4096	32768

Original Model Card

Model Card for Mistral-7B-v0.1

The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.

For full details of this model please read our paper and release blog post.

Model Architecture

Mistral-7B-v0.1 is a transformer model, with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

Troubleshooting

If you see the following error:

KeyError: 'mistral'

NotImplementedError: Cannot copy out of meta tensor; no data!

Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.

Notice

Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

Downloads last month: 84

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Collection including koesn/Mistral-7B-v0.1-GGUF

COMMERCIAL USE

Collection

7 items • Updated Mar 11, 2024 • 1

Paper for koesn/Mistral-7B-v0.1-GGUF

Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 61