Instructions to use Minachist/Ornith-1.0-35B-INT8-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minachist/Ornith-1.0-35B-INT8-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="Minachist/Ornith-1.0-35B-INT8-AutoRound")
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("Minachist/Ornith-1.0-35B-INT8-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("Minachist/Ornith-1.0-35B-INT8-AutoRound", 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 Minachist/Ornith-1.0-35B-INT8-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minachist/Ornith-1.0-35B-INT8-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minachist/Ornith-1.0-35B-INT8-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Minachist/Ornith-1.0-35B-INT8-AutoRound

SGLang

How to use Minachist/Ornith-1.0-35B-INT8-AutoRound 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 "Minachist/Ornith-1.0-35B-INT8-AutoRound" \
    --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": "Minachist/Ornith-1.0-35B-INT8-AutoRound",
		"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 "Minachist/Ornith-1.0-35B-INT8-AutoRound" \
        --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": "Minachist/Ornith-1.0-35B-INT8-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Minachist/Ornith-1.0-35B-INT8-AutoRound with Docker Model Runner:
```
docker model run hf.co/Minachist/Ornith-1.0-35B-INT8-AutoRound
```

Ornith-1.0-35B — W8A16 AutoRound (INT8 weight-only)

This is an unofficial W8A16 (8-bit weights, 16-bit activations) quantized version of deepreinforce-ai/Ornith-1.0-35B, created with AutoRound.

Ornith-1.0-35B is DeepReinforce AI's lightweight agentic-coding model.

Quantizing the routed experts and attention projections to INT8 shrinks the checkpoint from ~70 GB (BF16) to ~38 GB, so the model fits comfortably on 2×24 GB GPUs while keeping the output distribution very close to the original.

In addition to the quantization, this checkpoint bundles a re-trained MTP draft module for speculative decoding. The MTP weights originate from Qwen/Qwen3.5-35B-A3B and were fine-tuned against Ornith's outputs. See MTP draft module for more.

What is quantized

INT8 (per-output-channel, symmetric) is applied to the routed-expert MLPs (gate_up_proj, down_proj) and the full-attention projections. The following are kept at BF16:

embed_tokens, lm_head, the MoE router (mlp.gate), the shared expert (shared_expert), the linear-attention / gated-delta mixers (linear_attn), and the entire vision tower (visual).

In total ~30,760 / 31,181 linear modules are quantized. the rest stay BF16.

Quantization details

Field	Value
Base model	`deepreinforce-ai/Ornith-1.0-35B`
Method	AutoRound (`intel/auto-round`)
Scheme	W8A16
Bits	8
Group size	-1 (per-output-channel)
Symmetric	yes
Format	`auto_round` (gptq-style packing)
Unquantized layers	`embed_tokens`, `lm_head`, `mlp.gate`, `shared_expert`, `linear_attn`, `visual`
Calibration data	25 % `NeelNanda/pile-10k` + 75 % `codeparrot/github-code-clean`
Calibration samples	1024 (256 pile + 768 github-code)
Iterations	1000
Batch size	8
Sequence length	2048
GPU used for quant	2× RTX 3090

KLD results

Quality was verified by measuring the KL divergence of the next-token distribution against the original BF16 model, KL(P_bf16 ‖ Q_int8), over 131,072 tokens (128 passages × 1024 tokens from NeelNanda/pile-10k, held out from calibration). Lower is better.

Metric	Value
Mean KL	0.00348 nats
Median KL	0.00139 nats
99th-percentile KL	0.0321 nats
Reverse KL `KL(Q‖P)`	0.00354 nats
Top-1 agreement	97.5 %

MTP draft module

This checkpoint additionally ships a MTP draft module (model-mtp.safetensors) for speculative decoding. This module is not part of the official Ornith release. It was rebuilt and re-trained as described below.

MTP details

The MTP module here was initialized from the mtp.* tensors of the original Qwen/Qwen3.5-35B-A3B checkpoint and then fine-tuned to match Ornith-1.0-35B's output distribution (self-distillation, see below).
This is an unofficial community artifact. It is not affiliated with or endorsed by DeepReinforce AI or the Qwen team.

Training recipe (self-distillation)

Data: 26,100 prompts drawn from 8 public instruction datasets (EN/JA general instructions, code, math, dialogue). Only the prompts were used. Every supervision target is Ornith-1.0-35B's own generation, plus the captured backbone hidden states.

Prompt sources (prompts only)

theblackcat102/evol-codealpaca-v1, m-a-p/CodeFeedback-Filtered-Instruction, openai/gsm8k, meta-math/MetaMathQA, OpenAssistant/oasst1, CohereLabs/aya_dataset, kunishou/hh-rlhf-49k-ja, kunishou/oasst1-89k-ja

Measured performance

Single-stream decode on 2× RTX 3090 (TP=2), vLLM nightly (0.23.1rc1), this exact checkpoint:

Configuration	Decode tok/s	Acceptance rate	Per-position acceptance	Mean accepted length
No speculative decoding	≈ 166	—	—	1.0
+ MTP, `num_speculative_tokens=3`	≈ 267	0.66	0.836 / 0.649 / 0.496	2.98

MTP row: 160 held-out mixed instruction + code prompts × 512 tokens each.

Baseline row: 28 coding prompts × 1024 tokens each.

k=3 gave the best single-stream throughput in testing. k=2 yields a higher per-draft acceptance rate (≈ 0.70–0.75) at slightly lower throughput.

MTP quantization

The MTP routed experts (mtp.layers.0.mlp.experts) are INT8-quantized with GPTQ, using the same AutoRound scheme as the backbone. Everything else in the MTP block stays in BF16.

How to use

vLLM is recommended (tested with vllm/vllm-openai:nightly, 0.23.1rc1). To enable MTP speculative decoding:

vllm serve <this-repo> \
  --tensor-parallel-size 2 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

For text-only serving on 2×24 GB, --language-model-only --mamba-cache-mode align were used in testing.

If you're having quality issues while using MTP, it's likely a vLLM issue since MTP shouldn't degrade the output at all.

For more information, please see: this reddit thread, and this vLLM pull

Acknowledgements

Lorbus for the README.md format
Sam McLeod for the Docker / 2× 3090 P2P configurations
DeepReinforce AI for the base Ornith-1.0-35B model (post-trained on Qwen3.5-35B-A3B)
Intel AutoRound team for the quantization framework
vLLM project for the inference engine
Qwen for the base MTP model

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Safetensors

Model size

11B params

Tensor type

I32

BF16

F16

Model tree for Minachist/Ornith-1.0-35B-INT8-AutoRound

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(182)

this model