Instructions to use SC117/Ornith-1.0-35B-Heretic-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/Ornith-1.0-35B-Heretic-MTP with Transformers:

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

pipe = pipeline("text-generation", model="SC117/Ornith-1.0-35B-Heretic-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("SC117/Ornith-1.0-35B-Heretic-MTP")
model = AutoModelForMultimodalLM.from_pretrained("SC117/Ornith-1.0-35B-Heretic-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?"}
        ]
    },
]
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 SC117/Ornith-1.0-35B-Heretic-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Ornith-1.0-35B-Heretic-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": "SC117/Ornith-1.0-35B-Heretic-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SC117/Ornith-1.0-35B-Heretic-MTP

SGLang

How to use SC117/Ornith-1.0-35B-Heretic-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 "SC117/Ornith-1.0-35B-Heretic-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": "SC117/Ornith-1.0-35B-Heretic-MTP",
		"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 "SC117/Ornith-1.0-35B-Heretic-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": "SC117/Ornith-1.0-35B-Heretic-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SC117/Ornith-1.0-35B-Heretic-MTP with Docker Model Runner:
```
docker model run hf.co/SC117/Ornith-1.0-35B-Heretic-MTP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

HERETIC 1.4.0MTPAPEXMIT

Ornith-1.0-35B-Heretic-MTP-APEX

English | 📖 中文文档

Heretic Trial 63 BF16 merge · compatible MTP head · APEX GGUFs + mmproj

🐦 About this release

Ornith-1.0-35B is an agentic coding model from DeepReinforce AI, based on Qwen3.5 MoE. This experimental derivative merges the selected Heretic 1.4.0 Trial 63 LoRA into BF16 weights.

It also includes a compatible Qwen3.5 MTP head, a BF16 vision projector, and APEX mixed-precision GGUFs. Refusal-modified models can behave differently from the original; evaluate before deployment. License: MIT.

🧠 Model Details

Architecture	Qwen3.5 MoE, multimodal
Parameters	35B total, 3B active per token · 256 experts, 8 active
Layers	40 transformer layers + 1 MTP layer (785 BF16 tensors)
Context	262,144 tokens
Weights	BF16 safetensors, 17 shards; audited LoRA merge
License	MIT

⚡ Heretic Trial 63 of 120

Selected parameters: direction_index=21.66; attention o_proj range 1.55–2.05; MLP down_proj range 1.64–1.84.

Refusal prescreen / full evaluation	2 / 30 · 11 / 100
Validation KL mean / p95 / max	0.0242 / 0.1477 / 0.1648
Generation health	Passed · top-1 disagreement 2.0%

📊 BenchLocal Results (APEX-I-Compact)

Mode	ToolCall	BugFind	HermesAgent	Max / Effective
Thinking ↗	100	98	89	95.0 / 90.0
No Thinking ↗	97	97	85	92.2 / 76.2

Latest result cards: Thinking has 5 retry-penalty points; No Thinking has 16.

🚀 Usage

llama.cpp (vision + text)

hf download SC117/Ornith-1.0-35B-Heretic-MTP-APEX-GGUF --include "*.gguf" --local-dir ./models ./llama-server -m ./models/Ornith-1.0-35B-Heretic-MTP-APEX-I-Compact.gguf --mmproj ./models/mmproj-Ornith-1.0-35B-Heretic-MTP-BF16.gguf -ngl 99 -c 131072

🎛️ Recommended Settings

General / Coding

temperature=0.6, top_p=0.95, top_k=20, do_sample=true

💡 What is APEX?

These GGUFs use APEX, an MoE-aware mixed-precision quantization method. It allocates precision according to tensor role and layer position; the MTP layer remains Q8_0 and the matching vision projector remains BF16.

📦 APEX Quantization Tiers

File	Size	Best For
`*-APEX-I-Quality.gguf`	21.87 GiB	Highest quality
`*-APEX-I-Balanced.gguf`	24.33 GiB	Best all-rounder
`*-APEX-I-Compact.gguf`	16.14 GiB	Best quality/size ratio
`*-APEX-I-Mini.gguf`	13.29 GiB	Most compact

Citation

@misc{ornith-35b,
    title = {{Ornith-1.0-35B}: Agentic Coding, Open to All},
    url = {https://deep-reinforce.com/ornith_1_0.html},
    author = {{DeepReinforce Team}},
    year = {2026}
}

Downloads last month: 466

Safetensors

Model size

665k params

Tensor type

BF16

Model tree for SC117/Ornith-1.0-35B-Heretic-MTP

Base model

deepreinforce-ai/Ornith-1.0-35B

Finetuned

(12)

this model

Quantizations

3 models

Collection including SC117/Ornith-1.0-35B-Heretic-MTP

Ornith-1.0

Collection

a self-improving family of open-source models for agentic coding. • 5 items • Updated 1 day ago • 2

SC117
/

Ornith-1.0-35B-Heretic-MTP

Ornith-1.0-35B-Heretic-MTP-APEX

Links

Citation

Model tree for SC117/Ornith-1.0-35B-Heretic-MTP

Collection including SC117/Ornith-1.0-35B-Heretic-MTP

Ornith-1.0