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

Libraries

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

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

pipe = pipeline("text-generation", model="Ma7ee7/Meet7_0.6b_Exp")
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")
model = AutoModelForCausalLM.from_pretrained("Ma7ee7/Meet7_0.6b_Exp", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/Ma7ee7/Meet7_0.6b_Exp

SGLang

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

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Ma7ee7/Meet7_0.6b_Exp",
    max_seq_length=2048,
)

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

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