Instructions to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF",
	filename="Meissa-Qwen2.5-7B-Instruct.Q2_K.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 QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

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 QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

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 QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Meissa-Qwen2.5-7B-Instruct-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": "QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-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 QuantFactory/Meissa-Qwen2.5-7B-Instruct-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 QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Meissa-Qwen2.5-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF

This is quantized version of Orion-zhen/Meissa-Qwen2.5-7B-Instruct created using llama.cpp

Original Model Card

Meissa-Qwen2.5-7B-Instruct

Meissa is designated Lambda Orionis, forms Orion's head, and is a multiple star with a combined apparent magnitude of 3.33. Its name means the "shining one".

This model is fine tuned over writing and role playing datasets (maybe the first on qwen2.5-7b), aiming to enhance model's performance in novel writing and roleplaying.

The model is fine-tuned over Orion-zhen/Qwen2.5-7B-Instruct-Uncensored

Training details

I used SFT method. Datasets used are listed below:

anthracite-org/stheno-filtered-v1.1
MinervaAI/Aesir-Preview
Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
anthracite-org/nopm_claude_writing_fixed
Gryphe/Sonnet3.5-Charcard-Roleplay
nothingiisreal/DirtyWritingPrompts
Orion-zhen/tagged-pixiv-novel

Downloads last month: 875

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

Orion-zhen/Qwen2.5-7B-Instruct-Uncensored

Quantized

(14)

this model

QuantFactory
/

Meissa-Qwen2.5-7B-Instruct-GGUF

QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF

Original Model Card

Meissa-Qwen2.5-7B-Instruct

Training details

Model tree for QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF

Datasets used to train QuantFactory/Meissa-Qwen2.5-7B-Instruct-GGUF