Instructions to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP with Transformers:

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

pipe = pipeline("image-text-to-text", model="EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP")
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("EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP")
model = AutoModelForMultimodalLM.from_pretrained("EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP", 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 EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP",
		"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/EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP

SGLang

How to use EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP 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 "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP" \
    --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": "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP",
		"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 "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP" \
        --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": "EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP",
		"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 EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP with Docker Model Runner:
```
docker model run hf.co/EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ornith-1.5-35B-A3B-BigBang-MTP

A TIES merge of ornith-ai/Ornith-1.5-35B-A3B (agentic-coding RL) and endless-frontier/BigBang-v1 (general tune), both post-trains of Qwen/Qwen3.6-35B-A3B — plus a working MTP speculative-decoding head, which stock Ornith-1.5 does not have.

The MTP finding

Ornith-1.5-35B-A3B ships 785 mtp.* tensors that are random initialisation, not trained weights: every projection has std = 0.0200 with Gaussian kurtosis 3.0 (i.e. exactly initializer_range=0.02), and its norm weights sit near 0.02 instead of ~1. Used as a speculative draft it accepts only ~13% of tokens (pure chance). This model replaces that placeholder with the trained MTP head from Qwen3.6-35B-A3B, which transfers cleanly because Ornith's language tower is only ~0.2–1% away from Qwen3.6 (measured cosine per tensor group).

Measured with llama.cpp speculative decoding (Q4_K_M main + Q8_0 draft, RTX 3090):

draft head	acceptance	mean accepted run
Qwen3.6 grafted (this model)	0.55–0.75 (code high, chat lower)	3.2–4.0 tokens
Ornith-1.5 stock (random init)	~0.13	~1.5

Merge recipe

TIES (density 0.25, λ=1.0), computed in fp32 over the Qwen3.6-35B-A3B base, on all text weights.
MoE router gates: kept verbatim from Ornith (never averaged — routing is where naive MoE merges break).
Vision tower: kept verbatim (bit-identical between Ornith and Qwen3.6 anyway; this model keeps Qwen3.6's multimodal eyes).
mtp.*: Qwen3.6's trained head, verbatim (fused-expert layout, loads with the same Qwen3_5MoeForConditionalGeneration class).

Evaluation (Q4_K_M, single RTX 3090, temp 0.1)

	this merge	stock Ornith-1.5
15-task Python pass@1	15/15	15/15
perplexity (mixed code+prose)	3.34	3.41
generation speed (no draft)	131.0 tok/s	133.9 tok/s
speed with MTP draft	169.4 tok/s (+29%)	n/a (head is untrained)

Chat, instruction-following, translation and creative prompts remain coherent (spot-checked; e.g. Welsh translation is understandable but slightly unnatural). This is a small local eval, not a benchmark suite — treat it as a no-regression check plus the MTP head-to-head, not a leaderboard claim.

Notes and caveats

Reasoning is always on (<think>), inherited from Ornith/Qwen3.6. Give generous max_tokens.
Delta-interference probes (cosine between task vectors) showed Ornith's RL delta and BigBang's tune are near-orthogonal (cos ≈ +0.11), which is why the merge composes; this does not guarantee gains on any specific benchmark.
GGUF quants + the MTP draft GGUF: EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF
Licences: Ornith-1.5 is MIT; BigBang-v1 and Qwen3.6-35B-A3B are Apache-2.0. This merge is released under MIT with attribution to all three parents.

Merged and measured by EryriLabs (Dwain Barnes), 2026-08-20.