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")
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 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 on 2×24 GB GPUs while keeping the output distribution close to the original (see fidelity below).

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 details

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 %

How to use

vLLM is recommended.

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

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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

deepreinforce-ai/Ornith-1.0-35B

Quantized

(110)

this model