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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="filipealmeida/Mistral-7B-Instruct-v0.1-GGUF",
	filename="mistral-7b-instruct-v1.0-f16.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 filipealmeida/Mistral-7B-Instruct-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 filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

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 filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

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 filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

Use Docker

docker model run hf.co/filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

LM Studio
Jan

vLLM

How to use filipealmeida/Mistral-7B-Instruct-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "filipealmeida/Mistral-7B-Instruct-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": "filipealmeida/Mistral-7B-Instruct-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

Ollama
How to use filipealmeida/Mistral-7B-Instruct-v0.1-GGUF with Ollama:
```
ollama run hf.co/filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
```

Unsloth Studio

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

Docker Model Runner
How to use filipealmeida/Mistral-7B-Instruct-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull filipealmeida/Mistral-7B-Instruct-v0.1-GGUF:F16

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.1-GGUF-F16

List all available models

lemonade list

GGUF version of version of Mistral-7B-Instruct-v0.1

GGUF version of version of Mistral-7B-Instruct-v0.1 compatible with llama.cpp

This is the unquantized fp16 version of the model.

Model Card for Mistral-7B-Instruct-v0.1

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

For full details of this model please read our 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.

from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

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]"

encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)

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])

Model Architecture

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

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

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: 39

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

16-bit