Instructions to use DCAgent/a1-taskmaster2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DCAgent/a1-taskmaster2 with Transformers:

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

pipe = pipeline("text-generation", model="DCAgent/a1-taskmaster2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DCAgent/a1-taskmaster2")
model = AutoModelForCausalLM.from_pretrained("DCAgent/a1-taskmaster2")
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

vLLM

How to use DCAgent/a1-taskmaster2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DCAgent/a1-taskmaster2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DCAgent/a1-taskmaster2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DCAgent/a1-taskmaster2

SGLang

How to use DCAgent/a1-taskmaster2 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 "DCAgent/a1-taskmaster2" \
    --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": "DCAgent/a1-taskmaster2",
		"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 "DCAgent/a1-taskmaster2" \
        --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": "DCAgent/a1-taskmaster2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DCAgent/a1-taskmaster2 with Docker Model Runner:
```
docker model run hf.co/DCAgent/a1-taskmaster2
```

a1-taskmaster2 / run_summary.json

EtashGuha

Upload folder using huggingface_hub

2d7a441 verified about 2 months ago

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	{
	"agent_name": "e61ae9daad0187d5e2a362fb26853dd7ac8e987a_thinking_preprocessed",
	"training_start": null,
	"training_end": null,
	"created_by": "raoof1",
	"base_model_name": "Qwen/Qwen3-8B",
	"dataset_name": "/e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetash/snapshots/e61ae9daad0187d5e2a362fb26853dd7ac8e987a_thinking_preprocessed",
	"training_type": "SFT",
	"training_parameters": "https://huggingface.co/DCAgent/a1-taskmaster2/blob/main/config.json",
	"wandb_link": null,
	"traces_location_s3": null
	}