Instructions to use Ma7ee7/Meet8-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ma7ee7/Meet8-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="Ma7ee7/Meet8-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ma7ee7/Meet8-0.6B")
model = AutoModelForCausalLM.from_pretrained("Ma7ee7/Meet8-0.6B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ma7ee7/Meet8-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ma7ee7/Meet8-0.6B"
# 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/Meet8-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ma7ee7/Meet8-0.6B

SGLang

How to use Ma7ee7/Meet8-0.6B 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/Meet8-0.6B" \
    --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/Meet8-0.6B",
		"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/Meet8-0.6B" \
        --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/Meet8-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ma7ee7/Meet8-0.6B with Docker Model Runner:
```
docker model run hf.co/Ma7ee7/Meet8-0.6B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meet8-0.6B

An experimental full-parameter post-training run by Ma7ee7, starting from Qwen/Qwen3-0.6B-Base.

Training

Supervised fine-tuning on instruction, identity, human-written chat, generated FineWeb instruction examples, and generated creative writing.
Paired direct-answer and /think warmup using short reasoning examples.
Full-parameter GRPO on math, science, and logic tasks.
Approximately 3,500 total RL optimizer steps.

Usage

Use the saved tokenizer chat template. The following system message substantially improves identity and reasoning-mode compliance:

You are Meet8-0.6B, developed by Ma7ee7. If the user's message begins with /think, give a brief explanation inside ..., then give your final response outside the tags. Otherwise answer directly without thinking tags. Never return an empty answer.

Diagnostic results

Small held-out greedy evaluations, with 32 questions per task and mode:

Task	Direct accuracy	Think accuracy
GSM8K	12.50%	37.50%
ARC-Easy	81.25%	84.38%
Arithmetic	56.25%	84.38%
Procedural logic	56.25%	50.00%
RuleTaker	53.13%	46.88%
Knights and knaves	28.13%	15.63%
Countdown	3.13%	0.00%

These are small custom diagnostics, not standard full-split benchmark scores. Direct and think partitions may contain different questions. No successful standardized Base-versus-Meet8 comparison is available.

Limitations

Creative writing can enter severe repetition loops. Reasoning explanations can be incorrect despite appearing plausible. Identity and /think behavior depend strongly on the system prompt. Overall improvement over the base model has not been established.

Data and licensing

Training included HuggingFaceH4/no_robots, distributed under CC BY-NC 4.0. Consult upstream model and dataset licenses before reuse. Additional logic datasets and generated data were used; this card does not provide a complete provenance or overlap audit.

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for Ma7ee7/Meet8-0.6B

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(716)

this model

Quantizations

1 model

Ma7ee7
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Meet8-0.6B