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

Libraries

How to use Manoghn/voicecraft-mistral-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Manoghn/voicecraft-mistral-7b-gguf",
	filename="voicecraft-f16.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 Manoghn/voicecraft-mistral-7b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Manoghn/voicecraft-mistral-7b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf Manoghn/voicecraft-mistral-7b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Manoghn/voicecraft-mistral-7b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf Manoghn/voicecraft-mistral-7b-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 Manoghn/voicecraft-mistral-7b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf Manoghn/voicecraft-mistral-7b-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 Manoghn/voicecraft-mistral-7b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Manoghn/voicecraft-mistral-7b-gguf:F16

Use Docker

docker model run hf.co/Manoghn/voicecraft-mistral-7b-gguf:F16

LM Studio
Jan

vLLM

How to use Manoghn/voicecraft-mistral-7b-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Manoghn/voicecraft-mistral-7b-gguf:F16

Ollama
How to use Manoghn/voicecraft-mistral-7b-gguf with Ollama:
```
ollama run hf.co/Manoghn/voicecraft-mistral-7b-gguf:F16
```

Unsloth Studio

How to use Manoghn/voicecraft-mistral-7b-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 Manoghn/voicecraft-mistral-7b-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 Manoghn/voicecraft-mistral-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use Manoghn/voicecraft-mistral-7b-gguf with Docker Model Runner:
```
docker model run hf.co/Manoghn/voicecraft-mistral-7b-gguf:F16
```

Lemonade

How to use Manoghn/voicecraft-mistral-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Manoghn/voicecraft-mistral-7b-gguf:F16

Run and chat with the model

lemonade run user.voicecraft-mistral-7b-gguf-F16

List all available models

lemonade list

VoiceCraft GGUF - Ollama Ready

Quantized GGUF versions of VoiceCraft fine-tuned on LinkedIn post generation.

🚀 Quick Start

# Download model
huggingface-cli download Manoghn/voicecraft-mistral-7b-gguf --local-dir ./voicecraft

# Quantize to smaller size (recommended)
cd voicecraft
llama.cpp/quantize voicecraft-f16.gguf voicecraft-q4_k_m.gguf q4_k_m

# Import to Ollama
ollama create voicecraft -f Modelfile

# Run
ollama run voicecraft "Generate a LinkedIn post about AI trends"

📦 Files

File	Size	Description	Use Case
`voicecraft-f16.gguf`	~14GB	Full 16-bit precision	Highest quality, quantize locally
`Modelfile`	-	Ollama configuration	Pre-configured parameters

Recommended: Download the f16 version and quantize to q4_k_m on your machine (~4GB).

🎯 Model Information

Base Model: Mistral-7B-Instruct-v0.2
Fine-tuning: Custom LinkedIn post dataset (380 examples)
Training: 3 epochs, eval_loss: 0.7598
Specialization: 4 LinkedIn post types

Post Types

AI Trend/News - Technical and insightful
Career Learning - Reflective and growth-focused
Project Update - Achievement-focused with metrics
Personal Insight - Authentic and vulnerable

💻 Local Quantization

After downloading, quantize to your preferred size:

# 4-bit (smallest, recommended for most users)
llama.cpp/quantize voicecraft-f16.gguf voicecraft-q4_k_m.gguf q4_k_m

# 5-bit (balanced quality/size)
llama.cpp/quantize voicecraft-f16.gguf voicecraft-q5_k_m.gguf q5_k_m

# 8-bit (higher quality)
llama.cpp/quantize voicecraft-f16.gguf voicecraft-q8_0.gguf q8_0

🔧 Generation Parameters

Recommended settings (already in Modelfile):

temperature: 0.8
top_p: 0.95
repeat_penalty: 1.15
num_predict: 300

📚 Usage Example

ollama run voicecraft

>>> Post Type: Personal Insight
>>> Topic: Overcoming failure
>>> Style: Authentic and vulnerable
>>> Generate a LinkedIn post

🔗 Related

Original Model: Manoghn/voicecraft-mistral-7b
Base Model: mistralai/Mistral-7B-Instruct-v0.2

📄 License

Apache 2.0 (inherited from Mistral-7B)

🙏 Acknowledgments

Mistral AI for the base model
Hugging Face for training infrastructure
llama.cpp for GGUF conversion tools

Downloads last month: 28

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

16-bit

Model tree for Manoghn/voicecraft-mistral-7b-gguf

Base model

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

Quantized

(103)

this model