Instructions to use Ma7ee7/Meet7_0.6b_Exp_Q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Transformers:

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

pipe = pipeline("text-generation", model="Ma7ee7/Meet7_0.6b_Exp_Q8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ma7ee7/Meet7_0.6b_Exp_Q8")
model = AutoModelForCausalLM.from_pretrained("Ma7ee7/Meet7_0.6b_Exp_Q8")

llama-cpp-python

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ma7ee7/Meet7_0.6b_Exp_Q8",
	filename="Meet7_0.6b.Q8_0.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 Ma7ee7/Meet7_0.6b_Exp_Q8 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Ma7ee7/Meet7_0.6b_Exp_Q8: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 Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Ma7ee7/Meet7_0.6b_Exp_Q8: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 Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Use Docker

docker model run hf.co/Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

LM Studio
Jan

vLLM

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ma7ee7/Meet7_0.6b_Exp_Q8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ma7ee7/Meet7_0.6b_Exp_Q8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

SGLang

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 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 "Ma7ee7/Meet7_0.6b_Exp_Q8" \
    --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": "Ma7ee7/Meet7_0.6b_Exp_Q8",
		"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 "Ma7ee7/Meet7_0.6b_Exp_Q8" \
        --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": "Ma7ee7/Meet7_0.6b_Exp_Q8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Ollama:
```
ollama run hf.co/Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
```

Unsloth Studio new

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 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 Ma7ee7/Meet7_0.6b_Exp_Q8 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 Ma7ee7/Meet7_0.6b_Exp_Q8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ma7ee7/Meet7_0.6b_Exp_Q8 to start chatting

Pi new

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Docker Model Runner:
```
docker model run hf.co/Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0
```

Lemonade

How to use Ma7ee7/Meet7_0.6b_Exp_Q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ma7ee7/Meet7_0.6b_Exp_Q8:Q8_0

Run and chat with the model

lemonade run user.Meet7_0.6b_Exp_Q8-Q8_0

List all available models

lemonade list

Meet7 0.6B — Experimental

A continued fine-tune of Meet7 0.6B, trained at a lower learning rate on the same 600-sample dataset. Trades Meet7's sharp BoolQ spike for more balanced commonsense and reasoning gains across the board.

Benchmarks

0-shot evaluation, scores are acc_norm.

Task	Qwen3-0.6B (Base)	Meet7 0.6B	Experimental	Δ vs Base
BoolQ	0.3798	0.5554	0.3991	+01.93%
ARC Easy	0.3384	0.3952	0.3965	+05.81%
ARC Challenge	0.2841	0.3285	0.3259	+04.18%
HellaSwag	0.3981	0.4205	0.4265	+02.84%
PIQA	0.6338	0.6583	0.6687	+03.49%
Winogrande	0.5225	0.5201	0.5304	+00.79%

What these measure

BoolQ — Reading comprehension and yes/no factual grounding
ARC Easy / Challenge — Grade-school science reasoning; Challenge is the retrieval-resistant subset
HellaSwag — Commonsense sentence completion
PIQA — Physical world intuition
Winogrande — Commonsense pronoun resolution

vs Meet7 0.6B

This model is more balanced than Meet7. It outperforms Meet7 on HellaSwag, PIQA, and Winogrande — the physical and commonsense intuition tasks — at the cost of Meet7's large BoolQ advantage. If you need consistent commonsense reasoning, prefer this model. If yes/no QA is your primary use case, prefer Meet7.

Model Details


Developed by	Ma7ee7
License	Apache-2.0
Base model	Ma7ee7/Meet7_0.6b
Original base	unsloth/Qwen3-0.6B-unsloth-bnb-4bit
Training samples	600
Training	Continued LoRA fine-tune, lower LR