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

Libraries

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

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

pipe = pipeline("text-generation", model="Ma7ee7/Meet7_0.6b_Exp_Thinking")
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_Thinking")
model = AutoModelForCausalLM.from_pretrained("Ma7ee7/Meet7_0.6b_Exp_Thinking")
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

vLLM

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

Use Docker

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

SGLang

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

Unsloth Studio new

How to use Ma7ee7/Meet7_0.6b_Exp_Thinking 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_Thinking 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_Thinking 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_Thinking 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_Thinking",
    max_seq_length=2048,
)

Docker Model Runner
How to use Ma7ee7/Meet7_0.6b_Exp_Thinking with Docker Model Runner:
```
docker model run hf.co/Ma7ee7/Meet7_0.6b_Exp_Thinking
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meet7 0.6B — Experimental Thinking

A thinking trained variant of Meet7 0.6B Experimental, re-enabling Qwen3's built-in chain-of-thought reasoning at inference time.

Note: At 0.6B scale, thinking mode does not improve benchmark performance. (Except over base model which was tested with reasoning.) The model still lacks sufficient capacity to reason coherently across extended thought chains. For best results, use Meet7 Experimental without thinking mode, or Meet7 0.6B if BoolQ-style QA is your primary use case.

Benchmarks

0-shot evaluation across all four models in the Meet7 family. Scores are acc_norm.

Task	Base	Meet7	Experimental	Exp_Thinking
BoolQ	0.3798	0.5554	0.3991	0.3783
ARC Easy	0.3384	0.3952	0.3965	0.3662
ARC Challenge	0.2841	0.3285	0.3259	0.3012
HellaSwag	0.3981	0.4205	0.4265	0.4261
PIQA	0.6338	0.6583	0.6687	0.6540
Winogrande	0.5225	0.5201	0.5304	0.5241

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

Meet7 Model Family

Model	Strengths	Weaknesses
Meet7 0.6B	BoolQ (+17.56% vs base)	Less balanced overall
Meet7 Experimental	Best overall balance, wins 4/6 tasks	BoolQ regresses vs Meet7
Meet7 Exp_Thinking (this model)	Thinking mode enabled	(OUR) Weakest benchmark scores at 0.6B scale

Model Details


Developed by	Ma7ee7
License	Apache-2.0
Base model	Ma7ee7/Meet7_0.6b-experimental
Original base	unsloth/Qwen3-0.6B-unsloth-bnb-4bit
Training samples	1800
Thinking mode	Enabled (Qwen3 native)

Trained 2x faster with Unsloth and Hugging Face TRL.

Downloads last month: 27

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for Ma7ee7/Meet7_0.6b_Exp_Thinking

Quantizations

1 model

Collection including Ma7ee7/Meet7_0.6b_Exp_Thinking

Meet7

Collection

A Collection of Efficiently Fine-Tuned Models. • 6 items • Updated Mar 10