Instructions to use Migtito/mistral-7b-instruct-v0.2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Migtito/mistral-7b-instruct-v0.2-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Migtito/mistral-7b-instruct-v0.2-gguf",
	filename="mistral-7b-instruct-v0.2.Q8_0.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 Migtito/mistral-7b-instruct-v0.2-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

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 Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

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 Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

Use Docker

docker model run hf.co/Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

LM Studio
Jan

vLLM

How to use Migtito/mistral-7b-instruct-v0.2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Migtito/mistral-7b-instruct-v0.2-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": "Migtito/mistral-7b-instruct-v0.2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

Ollama
How to use Migtito/mistral-7b-instruct-v0.2-gguf with Ollama:
```
ollama run hf.co/Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
```

Unsloth Studio

How to use Migtito/mistral-7b-instruct-v0.2-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 Migtito/mistral-7b-instruct-v0.2-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 Migtito/mistral-7b-instruct-v0.2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Migtito/mistral-7b-instruct-v0.2-gguf to start chatting

Docker Model Runner
How to use Migtito/mistral-7b-instruct-v0.2-gguf with Docker Model Runner:
```
docker model run hf.co/Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0
```

Lemonade

How to use Migtito/mistral-7b-instruct-v0.2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Migtito/mistral-7b-instruct-v0.2-gguf:Q8_0

Run and chat with the model

lemonade run user.mistral-7b-instruct-v0.2-gguf-Q8_0

List all available models

lemonade list

Mistral 7B Instruct v0.2 - GGUF

Model creator: Mistral AI_
Original model: Mistral 7B Instruct v0.2

Model Card for Mistral-7B-Instruct-v0.2

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.

Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1

32k context window (vs 8k context in v0.1)
Rope-theta = 1e6
No Sliding-Window Attention

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

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Troubleshooting

If you see the following error:

Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'

Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers

This should not be required after transformers-v4.33.4.

Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

The Mistral AI Team

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

Downloads last month: 17

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

32-bit

Model tree for Migtito/mistral-7b-instruct-v0.2-gguf

Base model

mistralai/Mistral-7B-Instruct-v0.2

Quantized

(103)

this model

Paper for Migtito/mistral-7b-instruct-v0.2-gguf

Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 60