Instructions to use itapitarules/OpenCore-1M-MTP-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itapitarules/OpenCore-1M-MTP-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="itapitarules/OpenCore-1M-MTP-2B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("itapitarules/OpenCore-1M-MTP-2B")
model = AutoModelForMultimodalLM.from_pretrained("itapitarules/OpenCore-1M-MTP-2B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use itapitarules/OpenCore-1M-MTP-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itapitarules/OpenCore-1M-MTP-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itapitarules/OpenCore-1M-MTP-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/itapitarules/OpenCore-1M-MTP-2B

SGLang

How to use itapitarules/OpenCore-1M-MTP-2B 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 "itapitarules/OpenCore-1M-MTP-2B" \
    --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": "itapitarules/OpenCore-1M-MTP-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "itapitarules/OpenCore-1M-MTP-2B" \
        --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": "itapitarules/OpenCore-1M-MTP-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use itapitarules/OpenCore-1M-MTP-2B with Docker Model Runner:
```
docker model run hf.co/itapitarules/OpenCore-1M-MTP-2B
```

OpenCore 1M MTP 2B

This is a final unquantized BF16 OpenCore 2B edition. It is one standard Qwen3.5 multimodal model, not two models glued together. Every released parameter is on a normal executable language, vision, or MTP path. This edition preserves the strongest intact pretrained Qwen3.5 2B weights after candidate fine-tunes were rejected for coding regressions. OpenCore workflow and continual-learning behavior is provided by the shared companion, not a hidden runtime prompt.

Exact contents

Total serialized parameters: 2,274,069,824
Language parameters: 1,881,825,088
Vision parameters: 331,416,576
Native one-block MTP parameters: 60,828,160
Main plus vision-projector GGUF size: 4.569 GB
Tensor types: BF16 plus only architecture-required F32 auxiliaries; no quantization
Selectable context metadata: 1 to 1,010,000 tokens
Original trained context: 262,144 tokens
Measured selected MTP draft maximum: 4
Measured local median generation speed: 133.564 tokens/second
Coding/tool evaluation: exact executable task success 100%, tool validity 100%; release promoted

The million-token value is the loader-selectable ceiling. Memory use grows with the context selected, so start at 32K on a 12 GB GPU and increase only when the task needs it. The original trained window is 262,144 tokens; longer selections are extrapolation and are not a claim of equal quality across the full ceiling.

Files

OpenCore-1M-MTP-2B-BF16.gguf: llama.cpp / LM Studio main BF16 GGUF, including native MTP tensors
OpenCore-1M-MTP-2B-BF16-mmproj.gguf: BF16 multimodal vision projector
OpenCore-1M-MTP-2B-BF16-HF/: exact merged Hugging Face BF16 model
Modelfile: Ollama text-mode import, with no hidden SYSTEM prompt
run-opencore.ps1: llama.cpp launcher using GPU offload, Flash Attention and MTP
RELEASE_INDEX.json: checksums, sizes, runtime status and architecture facts

Run

LM Studio: import the main GGUF, select its matching projector, and choose context at load time. llama.cpp from this folder:

.\run-opencore.ps1 -Context 32768 -DraftMax 4

Ollama text-mode import:

ollama create opencore-1m-mtp-2b -f Modelfile

Ollama backend support for external multimodal projectors and native Qwen3.5 MTP may differ from llama.cpp. The full-feature validation path is recorded in the release index.

Real cross-harness learning

A GGUF file is immutable and cannot safely rewrite itself during generation. OpenCore's companion stores only objectively successful interactions in the shared SQLite store, trains a candidate, runs protected quality gates, rebuilds a fresh BF16/GGUF generation, and atomically promotes it only when it does not regress. A rejected or failed build leaves the current model untouched and supports rollback. The edition config is configs\continual-2b.json.

python scripts\background_learn.py --gguf-config configs\continual-2b.json

The detailed measured reports are artifacts\opencore-2b-release-final-speed.json and artifacts\opencore-2b-release-final-native-rope.json.

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Safetensors

Model size

2B params

Tensor type

F32

BF16