Instructions to use MTEnt/dot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MTEnt/dot with Transformers:

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

pipe = pipeline("text-generation", model="MTEnt/dot")
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("MTEnt/dot")
model = AutoModelForCausalLM.from_pretrained("MTEnt/dot", 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 MTEnt/dot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MTEnt/dot

SGLang

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

Docker Model Runner
How to use MTEnt/dot with Docker Model Runner:
```
docker model run hf.co/MTEnt/dot
```

dot

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{
  "release": "Dot-v0.4-Thinking",
  "checkpoint_step": 938,
  "training_tokens": 8642015,
  "evaluation_scope": {
    "task_families": 8,
    "relationship_to_training": "same executable generators, separately seeded and hash-disjoint records",
    "warning": "This is targeted curriculum evaluation, not a broad reasoning or ARC-AGI benchmark."
  },
  "teacher_forced_4096": {
    "baseline_zero_gate": {
      "response_nll": 2.1283936659,
      "response_perplexity": 8.40136,
      "teacher_forced_token_accuracy": 0.666026,
      "teacher_forced_exact_response_rate": 0.0,
      "reasoning_nll": 2.511300,
      "reasoning_token_accuracy": 0.604705,
      "answer_nll": 0.0667919,
      "answer_token_accuracy": 0.9960501
    },
    "dot_v0_4": {
      "response_nll": 0.01736697,
      "response_perplexity": 1.017519,
      "teacher_forced_token_accuracy": 0.9942831,
      "teacher_forced_exact_response_rate": 0.8776855,
      "reasoning_nll": 0.0205677,
      "reasoning_token_accuracy": 0.9932305,
      "answer_nll": 0.00011697,
      "answer_token_accuracy": 0.9999480
    },
    "response_nll_change": -2.1110267
  },
  "free_running_256": {
    "thinking_envelope_valid_rate": 1.0,
    "nonempty_reasoning_rate": 1.0,
    "mean_reasoning_tokens": 23.8633,
    "mean_total_generated_tokens": 28.5977,
    "truncated_before_think_close_rate": 0.0,
    "final_answer_exact_match": 0.96875,
    "correct": 248,
    "examples": 256
  },
  "adversarial_spatial_probe_64": {
    "suite_relationship": "new spatial generators outside the v0.4 training curriculum",
    "final_answer_exact_match": 0.21875,
    "correct": 14,
    "examples": 64,
    "thinking_envelope_valid_rate": 0.875,
    "truncation_rate": 0.109375,
    "orientation_exact_match": 0.0,
    "warning": "The larger 256-case run was stopped after a 15-minute timeout caused by uncached verbose generation."
  }
}