Instructions to use laion/tt-x1_lr-lr4e6-30-30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/tt-x1_lr-lr4e6-30-30B with Transformers:

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

pipe = pipeline("text-generation", model="laion/tt-x1_lr-lr4e6-30-30B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laion/tt-x1_lr-lr4e6-30-30B")
model = AutoModelForCausalLM.from_pretrained("laion/tt-x1_lr-lr4e6-30-30B", 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 laion/tt-x1_lr-lr4e6-30-30B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/tt-x1_lr-lr4e6-30-30B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/tt-x1_lr-lr4e6-30-30B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/tt-x1_lr-lr4e6-30-30B

SGLang

How to use laion/tt-x1_lr-lr4e6-30-30B 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 "laion/tt-x1_lr-lr4e6-30-30B" \
    --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": "laion/tt-x1_lr-lr4e6-30-30B",
		"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 "laion/tt-x1_lr-lr4e6-30-30B" \
        --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": "laion/tt-x1_lr-lr4e6-30-30B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/tt-x1_lr-lr4e6-30-30B with Docker Model Runner:
```
docker model run hf.co/laion/tt-x1_lr-lr4e6-30-30B
```

tt-x1_lr-lr4e6 (TaskTrove X1 lr=4e-6, global_step_30)

X1 learning-rate ablation arm (lr = 4e-6) of the TaskTrove hyperparameter sweep.

Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct
Harness: terminus-2 agentic RL
Source: DCAgent/exp_rpt_multifile (TaskTrove, pytest verifier, pass_ratio reward shaping)
Geometry: 6 nodes x 4 GH200, Jupiter (JSC)

Status

TERMINATED by owner at step 36/80 after reward collapsed to exactly 0.000 at steps 33-36 (a hard collapse, not the gradual decay expected from an over-large learning rate). This is not a completed / valid X1 result: it did not reach its 80-step horizon. Selected checkpoint = global_step_30, the trailing-5 EMA peak (0.1700) among saved exports and the last healthy checkpoint before the step-33 collapse. See training_logs/report.md for the full curve.

Training Traces

Companion trial-level trace dataset: https://huggingface.co/datasets/penfever/tt-x1_lr-lr4e6

training_logs/

Per-step metric surface, the parse_skyrl_metrics.py analysis report, and the reward plot (metrics.csv, report.md, reward_plot.png).

Downloads last month: -

Safetensors

Model size

31B params

Tensor type

BF16

Model tree for laion/tt-x1_lr-lr4e6-30-30B

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Finetuned

(71)

this model