Instructions to use nhotin/vistral7B-chat-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nhotin/vistral7B-chat-gguf with Transformers:

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

pipe = pipeline("text-generation", model="nhotin/vistral7B-chat-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nhotin/vistral7B-chat-gguf")
model = AutoModelForCausalLM.from_pretrained("nhotin/vistral7B-chat-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use nhotin/vistral7B-chat-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nhotin/vistral7B-chat-gguf",
	filename="ggml-vistral-7B-chat-q8.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nhotin/vistral7B-chat-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nhotin/vistral7B-chat-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf nhotin/vistral7B-chat-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nhotin/vistral7B-chat-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf nhotin/vistral7B-chat-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 nhotin/vistral7B-chat-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf nhotin/vistral7B-chat-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 nhotin/vistral7B-chat-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nhotin/vistral7B-chat-gguf:F16

Use Docker

docker model run hf.co/nhotin/vistral7B-chat-gguf:F16

LM Studio
Jan

vLLM

How to use nhotin/vistral7B-chat-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nhotin/vistral7B-chat-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": "nhotin/vistral7B-chat-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nhotin/vistral7B-chat-gguf:F16

SGLang

How to use nhotin/vistral7B-chat-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 "nhotin/vistral7B-chat-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nhotin/vistral7B-chat-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "nhotin/vistral7B-chat-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nhotin/vistral7B-chat-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nhotin/vistral7B-chat-gguf with Ollama:
```
ollama run hf.co/nhotin/vistral7B-chat-gguf:F16
```

Unsloth Studio new

How to use nhotin/vistral7B-chat-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 nhotin/vistral7B-chat-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 nhotin/vistral7B-chat-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nhotin/vistral7B-chat-gguf to start chatting

Docker Model Runner
How to use nhotin/vistral7B-chat-gguf with Docker Model Runner:
```
docker model run hf.co/nhotin/vistral7B-chat-gguf:F16
```

Lemonade

How to use nhotin/vistral7B-chat-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nhotin/vistral7B-chat-gguf:F16

Run and chat with the model

lemonade run user.vistral7B-chat-gguf-F16

List all available models

lemonade list

Model Card for Vistral-7B-Chat

Model Details

Model Name: Vistral-7B-Chat
Version: 1.0
Model Type: Causal Language Model
Architecture: Transformer-based model with 7 billion parameters
Quantization: 8-bit quantized for efficiency

Usage

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "nhotin/vistral7B-chat-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Your text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 19

Safetensors

Model size

7B params

Tensor type

F32

F16