Instructions to use sayhan/Mistral_Pro_8B_v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="sayhan/Mistral_Pro_8B_v0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sayhan/Mistral_Pro_8B_v0.1-GGUF", dtype="auto")

llama-cpp-python

How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sayhan/Mistral_Pro_8B_v0.1-GGUF",
	filename="mistral_pro_8b_v0.1.Q2_K.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 sayhan/Mistral_Pro_8B_v0.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/Mistral_Pro_8B_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-server -hf sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/Mistral_Pro_8B_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 sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sayhan/Mistral_Pro_8B_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 sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayhan/Mistral_Pro_8B_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": "sayhan/Mistral_Pro_8B_v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M

SGLang

How to use sayhan/Mistral_Pro_8B_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 "sayhan/Mistral_Pro_8B_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": "sayhan/Mistral_Pro_8B_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 "sayhan/Mistral_Pro_8B_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": "sayhan/Mistral_Pro_8B_v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with Ollama:
```
ollama run hf.co/sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use sayhan/Mistral_Pro_8B_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 sayhan/Mistral_Pro_8B_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 sayhan/Mistral_Pro_8B_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 sayhan/Mistral_Pro_8B_v0.1-GGUF to start chatting

Docker Model Runner
How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M
```

Lemonade

How to use sayhan/Mistral_Pro_8B_v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayhan/Mistral_Pro_8B_v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral_Pro_8B_v0.1-GGUF-Q4_K_M

List all available models

lemonade list

Mistral Pro 8B v0.1

Model creator: TencentARC
Original model: Mistral_Pro_8B_v0.1-7b-it

Description

This repo contains GGUF format model files for TencentARC's Mistral Pro 8B v0.1

Original model

Developed by: TencentARC

Description

Model Description

Mistral-Pro is a progressive version of the original Mistral model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.

Development and Training

Developed by Tencent's ARC Lab, Mistral-Pro is an 8 billion parameter model. It's an expansion of Mistral-7B, further trained on code and math corpora.

Intended Use

This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages.

Performance

Mistral_Pro_8B_v0.1 showcases superior performance on a range of benchmarks. It enhances the code and math performance of Mistral. Furthermore, it matches the performance of the recently dominant model, Gemma.

Overall Performance on Languages, math and code tasks

Model	ARC	Hellaswag	MMLU	TruthfulQA	Winogrande	GSM8K	HumanEval
Gemma-7B	61.9	82.2	64.6	44.8	79.0	50.9	32.3
Mistral-7B	60.8	83.3	62.7	42.6	78.0	39.2	28.7
Mistral_Pro_8B_v0.1	63.2	82.6	60.6	48.3	78.9	50.6	32.9

Limitations

While Mistral-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.

Ethical Considerations

Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.

Quantizon types

quantization method	bits	size	description	recommended
Q2_K	2	3.36	very small, very high quality loss	❌
Q3_K_S	3	3.91 GB	very small, high quality loss	❌
Q3_K_M	3	4.35 GB	small, substantial quality loss	❌
Q3_K_L	3	4.74 GB	small, substantial quality loss	❌
Q4_0	4	5.09 GB	legacy; small, very high quality loss	❌
Q4_K_S	4	5.13 GB	medium, balanced quality	✅
Q4_K_M	4	5.42 GB	medium, balanced quality	✅
Q5_0	5	6.20 GB	legacy; medium, balanced quality	❌
Q5_K_S	5	6.20 GB	large, low quality loss	✅
Q5_K_M	5	6.36 GB	large, very low quality loss	✅
Q6_K	6	7.37 GB	very large, extremely low quality loss	❌
Q8_0	8	9.55 GB	very large, extremely low quality loss	❌
FP16	16	18 GB	enormous, negligible quality loss	❌

Usage

You can use this model with the latest builds of LM Studio and llama.cpp.
If you're new to the world of large language models, I recommend starting with LM Studio.

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GGUF

Model size

9B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for sayhan/Mistral_Pro_8B_v0.1-GGUF

Base model

TencentARC/Mistral_Pro_8B_v0.1

Quantized

(4)

this model

sayhan
/

Mistral_Pro_8B_v0.1-GGUF

Mistral Pro 8B v0.1

Description

Original model

Description

Model Description

Development and Training

Intended Use

Performance

Overall Performance on Languages, math and code tasks

Limitations

Ethical Considerations

Quantizon types

Usage

Model tree for sayhan/Mistral_Pro_8B_v0.1-GGUF

Dataset used to train sayhan/Mistral_Pro_8B_v0.1-GGUF