Instructions to use AKASH2393/mistral-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AKASH2393/mistral-finetuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AKASH2393/mistral-finetuned",
	filename="mistral-finetuned.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 AKASH2393/mistral-finetuned with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AKASH2393/mistral-finetuned
# Run inference directly in the terminal:
llama-cli -hf AKASH2393/mistral-finetuned

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AKASH2393/mistral-finetuned
# Run inference directly in the terminal:
llama-cli -hf AKASH2393/mistral-finetuned

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 AKASH2393/mistral-finetuned
# Run inference directly in the terminal:
./llama-cli -hf AKASH2393/mistral-finetuned

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 AKASH2393/mistral-finetuned
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AKASH2393/mistral-finetuned

Use Docker

docker model run hf.co/AKASH2393/mistral-finetuned

LM Studio
Jan

vLLM

How to use AKASH2393/mistral-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AKASH2393/mistral-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AKASH2393/mistral-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AKASH2393/mistral-finetuned

Ollama
How to use AKASH2393/mistral-finetuned with Ollama:
```
ollama run hf.co/AKASH2393/mistral-finetuned
```

Unsloth Studio new

How to use AKASH2393/mistral-finetuned 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 AKASH2393/mistral-finetuned 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 AKASH2393/mistral-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AKASH2393/mistral-finetuned to start chatting

Docker Model Runner
How to use AKASH2393/mistral-finetuned with Docker Model Runner:
```
docker model run hf.co/AKASH2393/mistral-finetuned
```

Lemonade

How to use AKASH2393/mistral-finetuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AKASH2393/mistral-finetuned

Run and chat with the model

lemonade run user.mistral-finetuned-{{QUANT_TAG}}

List all available models

lemonade list

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: 7

Safetensors

Model size

7B params

Tensor type

BF16

Paper for AKASH2393/mistral-finetuned

Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 58