Instructions to use saulpzh/Meta-Task-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saulpzh/Meta-Task-32B with Transformers:

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

pipe = pipeline("text-generation", model="saulpzh/Meta-Task-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saulpzh/Meta-Task-32B")
model = AutoModelForCausalLM.from_pretrained("saulpzh/Meta-Task-32B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use saulpzh/Meta-Task-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saulpzh/Meta-Task-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saulpzh/Meta-Task-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saulpzh/Meta-Task-32B

SGLang

How to use saulpzh/Meta-Task-32B 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 "saulpzh/Meta-Task-32B" \
    --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": "saulpzh/Meta-Task-32B",
		"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 "saulpzh/Meta-Task-32B" \
        --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": "saulpzh/Meta-Task-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saulpzh/Meta-Task-32B with Docker Model Runner:
```
docker model run hf.co/saulpzh/Meta-Task-32B
```

saulpzh commited on 2 days ago

Commit

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@@ -16,6 +16,10 @@ tags:
 [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) fine-tuned on 3,221 trajectories synthesized
 by the Meta-Task framework, for solving terminal tasks.
 ## Results
 Terminal-Bench 2.0, Terminus-2 scaffold, non-thinking mode: **31.8 %** Avg Pass@1

 [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) fine-tuned on 3,221 trajectories synthesized
 by the Meta-Task framework, for solving terminal tasks.
+This model accompanies the paper **"Meta-Task: Turning Terminal Task Synthesis into a Terminal
+Task for Scalable Agent Training"** ([arXiv:2607.27929](https://arxiv.org/abs/2607.27929)).
+Official code: https://github.com/goodmanpzh/meta-task
 ## Results
 Terminal-Bench 2.0, Terminus-2 scaffold, non-thinking mode: **31.8 %** Avg Pass@1