Instructions to use programasweights/paw-4b-qwen3-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use programasweights/paw-4b-qwen3-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="programasweights/paw-4b-qwen3-0.6b")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("programasweights/paw-4b-qwen3-0.6b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use programasweights/paw-4b-qwen3-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "programasweights/paw-4b-qwen3-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programasweights/paw-4b-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/programasweights/paw-4b-qwen3-0.6b

SGLang

How to use programasweights/paw-4b-qwen3-0.6b 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 "programasweights/paw-4b-qwen3-0.6b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programasweights/paw-4b-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "programasweights/paw-4b-qwen3-0.6b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programasweights/paw-4b-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use programasweights/paw-4b-qwen3-0.6b with Docker Model Runner:
```
docker model run hf.co/programasweights/paw-4b-qwen3-0.6b
```

paw-4b-qwen3-0.6b / meta.json

yuntian-deng

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	{
	"opt_step": 210969,
	"timestamp": 1775534189.1305041,
	"compiler_model": "train_runs_7m_06b_376k_cont_9m_88k_freezecomp_40k_e1done_training_mar31_85k_975m_25k/big_run_06b.latest/compiler",
	"interpreter_model": "Qwen/Qwen3-0.6B",
	"freeze_interpreter": true,
	"freeze_compiler": true,
	"reward_type": "gt_logprob",
	"prefix_steps": 64,
	"prefix_loss_coef": 1.0,
	"compiler_rollout_reg_coef": 0.0,
	"compiler_prompt_style": "minimal",
	"num_rollouts": 1,
	"debug_overfit_one": false,
	"debug_example_idx": 0,
	"debug_overfit_n": 0,
	"lora_rank": 64,
	"lora_alpha": 16.0,
	"lora_target_modules": [
	"q_proj",
	"k_proj",
	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj"
	],
	"lora_num_bases": 64
	}