Instructions to use AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16")
model = AutoModelForImageTextToText.from_pretrained("AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16")
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

vLLM

How to use AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16",
		"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/AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16

SGLang

How to use AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16 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 "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16" \
    --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": "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16",
		"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 "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16" \
        --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": "AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16",
		"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 AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16 with Docker Model Runner:
```
docker model run hf.co/AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16
```

UI-Venus-1.5-2B-NOESIS-BF16

BF16 dtype-repack of inclusionAI/UI-Venus-1.5-2B — original FP16 floating-point weights losslessly cast to bfloat16 for LoRA / DoRA / PEFT compatibility and reduced disk footprint. The model architecture, parameter values, tokenizer, and configuration are identical to upstream — only the IEEE-754 storage dtype was changed.

License preserved end-to-end — see LICENSE in this repo for the full text and attribution chain.

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @AMAImediacom
NOESIS version: v15.8
Repack date: 2026-05-19

Summary

End-to-end GUI agent for autonomous UI navigation and element grounding. Trained via 4-stage pipeline (Mid-Train → Offline-RL → Online-RL → Model-Merge) on 10B GUI tokens across 30+ datasets. SOTA on ScreenSpot-Pro 57.7%, VenusBench-GD, OSWorld-G, AndroidWorld/Lab, WebVoyager.

Use case inside NOESIS

GUI agent / screenshot understanding / UI element grounding. Inside NOESIS this is an out-of-scope sibling — kept for general agent research, not the dubbing pipeline.

What changed vs upstream

Aspect	Upstream	This bundle
Floating-point storage dtype	FP16	bfloat16
`config.json` `torch_dtype`	as-is	`bfloat16`
`model.safetensors.index.json` `total_size`	as-is	recomputed
Tokenizer / chat template / modeling code	as-is	unchanged
Number of parameters	as-is	unchanged
Value-level transformation beyond dtype cast	—	none
Disk size	4.6 GB	4.6 GB

Architecture

Property	Value
Immediate parent	`inclusionAI/UI-Venus-1.5-2B`
Architecture	`Qwen3VLForConditionalGeneration`
Architecture base / lineage	Qwen3-VL-2B (4-stage post-training: Mid-Train 10B tok GUI → Offline-RL → Online-RL → Model-Merge)
Parameters	~2B (dense)
Hidden size	see text_config in config.json
Num hidden layers	see text_config in config.json
Attention heads / KV heads	see text_config in config.json
Vocab size	see text_config in config.json
Max position embeddings	see text_config in config.json
Format	bfloat16
Bundle size on disk	4.6 GB
License	Apache License 2.0
Project page	https://ui-venus.github.io/UI-Venus-1.5
Paper / arxiv	arxiv:2602.09082

Repack tooling

CPU-only sharded repack via repack_fp32_to_bf16.py — reads each shard with safetensors.safe_open, casts floating-point tensors to torch.bfloat16, rewrites the shard, updates the index manifest. No GPU involvement, no value-level transformation beyond the IEEE-754 dtype cast.

Performance reference (RTX 3060 laptop, NVMe SSD):

Single 5 GB FP32 shard cast → ~28-40 sec
Full 4.6 GB → 4.6 GB in 1 pass, sharded

Use cases (for the BF16 bundle)

✅ LoRA / DoRA / IA³ fine-tuning that requires a dtype=torch.bfloat16 base
✅ Bitsandbytes NF4 / AWQ-INT4 / GPTQ quantization (these tools prefer BF16 input)
✅ Inference on Ampere+ / MI200+ hardware with native BF16 support
✅ KD-teacher (forward-only) where BF16 storage saves bandwidth
❌ Full-parameter fine-tuning of weights — use FP32/BF16 master weights pattern; storage dtype alone is insufficient

Quick start

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "AMAImedia/UI-Venus-1.5-2B-NOESIS-BF16"

tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
).eval()

Sealed rules (NOESIS DHCF-FNO)

R-DTYPE-REPACK-BF16 — pure IEEE-754 dtype cast from FP16 to bfloat16. No value-level transformation, no LoRA merge, no architectural change. Equivalent to loading upstream with dtype=torch.bfloat16 and saving, but materialised on disk.
R-APACHE-CLEAN — upstream Apache License 2.0 preserved end-to-end via the LICENSE file in this repo. AMAImedia adds only a derivative-work notice for the repack step.
R-NO-VALUE-TRANSFORM — no fine-tuning, no distillation, no merge has been applied between upstream and this repo. Outputs are bit-for-bit equivalent up to the precision difference of the dtype cast.

License & attribution

This bundle inherits Apache License 2.0 from inclusionAI/UI-Venus-1.5-2B. Original model card, citation, and attribution from upstream apply without modification. See LICENSE in this repo for the complete text plus the NOESIS derivative-work NOTICE.

Citation

@misc{noesis2026uivenus152bnoesisbf16bf16,
  title  = {NOESIS DHCF-FNO :: UI-Venus-1.5-2B-NOESIS-BF16 — BF16 dtype-repack derivative},
  author = {Bolotnikov, Ilia and AMAImedia},
  year   = {2026},
  note   = {BF16 dtype-repack of inclusionAI/UI-Venus-1.5-2B for LoRA / PEFT
            compatibility. 4.6 GB on disk, Apache License 2.0
            preserved end-to-end.},
  url    = {https://huggingface.co/AMAImedia/UI-Venus-1.5-2B-NOESIS-BF16}
}

Please also cite the upstream model when using this bundle. See the upstream README and LICENSE in this repo for citation requirements.

Author

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @AMAImediacom
NOESIS version: v15.8
Repack date: 2026-05-19
HF repo: AMAImedia/UI-Venus-1.5-2B-NOESIS-BF16
Upstream: inclusionAI/UI-Venus-1.5-2B

Produced 2026-05-19 by NOESIS DHCF-FNO v15.8 — AMAImedia.com

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16

Base model

inclusionAI/UI-Venus-1.5-2B

Finetuned

(2)

this model

Paper for AMAImedia/Qwen3-VL-2B-UI-Venus-NOESIS-BF16

UI-Venus-1.5 Technical Report

Paper • 2602.09082 • Published Feb 9 • 157