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 / eval /spatial-process-stage1-v0.5.json

MTEnt

Document and load Dot v0.5 spatial process Stage 1

2cb6966 verified 2 months ago

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{
  "release": "Dot v0.5 Spatial Process - Stage 1",
  "checkpoint": "stage1k-v053-step-000003",
  "source_step": 3,
  "weights_sha256": "592608e67ab35be73e911b50c981117d832fb6969b9dd1267dea415c18d969df",
  "spatial_primitive_generation_256": {
    "thinking_envelope_valid_rate": 1.0,
    "final_answer_exact_match": 0.99609375,
    "complete_trace_rate": 1.0,
    "exact_trace_rate": 0.99609375,
    "transition_accuracy": 0.99609375
  },
  "non_spatial_generation_256": {
    "thinking_envelope_valid_rate": 1.0,
    "final_answer_exact_match": 0.98046875,
    "weakest_categories": {
      "register_execution": 0.9428571428571428,
      "directed_reachability": 0.9545454545454546,
      "state_supersession": 0.9714285714285714
    }
  },
  "local_runtime": {
    "profile": "NF4 double-quantized unchanged backbone; BF16 repair layers and recurrent core",
    "cuda_allocated_bytes_after_load": 10011683840,
    "cuda_free_bytes_after_load": 5493489664,
    "cpu_fallback": false,
    "artifact_hash_verified": true
  },
  "arc_agi_3_development_integration": {
    "official_fixture_count": 2,
    "solved": 0,
    "replay_integrity_valid": true,
    "warning": "Development fixtures measure integration only and are not an ARC-AGI-3 benchmark score."
  }
}

	{
	"release": "Dot v0.5 Spatial Process - Stage 1",
	"checkpoint": "stage1k-v053-step-000003",
	"source_step": 3,
	"weights_sha256": "592608e67ab35be73e911b50c981117d832fb6969b9dd1267dea415c18d969df",
	"spatial_primitive_generation_256": {
	"thinking_envelope_valid_rate": 1.0,
	"final_answer_exact_match": 0.99609375,
	"complete_trace_rate": 1.0,
	"exact_trace_rate": 0.99609375,
	"transition_accuracy": 0.99609375
	},
	"non_spatial_generation_256": {
	"thinking_envelope_valid_rate": 1.0,
	"final_answer_exact_match": 0.98046875,
	"weakest_categories": {
	"register_execution": 0.9428571428571428,
	"directed_reachability": 0.9545454545454546,
	"state_supersession": 0.9714285714285714
	}
	},
	"local_runtime": {
	"profile": "NF4 double-quantized unchanged backbone; BF16 repair layers and recurrent core",
	"cuda_allocated_bytes_after_load": 10011683840,
	"cuda_free_bytes_after_load": 5493489664,
	"cpu_fallback": false,
	"artifact_hash_verified": true
	},
	"arc_agi_3_development_integration": {
	"official_fixture_count": 2,
	"solved": 0,
	"replay_integrity_valid": true,
	"warning": "Development fixtures measure integration only and are not an ARC-AGI-3 benchmark score."
	}
	}