Instructions to use r0b0tlab/Hy3-295B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r0b0tlab/Hy3-295B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="r0b0tlab/Hy3-295B-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("r0b0tlab/Hy3-295B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("r0b0tlab/Hy3-295B-NVFP4", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r0b0tlab/Hy3-295B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r0b0tlab/Hy3-295B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r0b0tlab/Hy3-295B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/r0b0tlab/Hy3-295B-NVFP4

SGLang

How to use r0b0tlab/Hy3-295B-NVFP4 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 "r0b0tlab/Hy3-295B-NVFP4" \
    --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": "r0b0tlab/Hy3-295B-NVFP4",
		"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 "r0b0tlab/Hy3-295B-NVFP4" \
        --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": "r0b0tlab/Hy3-295B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use r0b0tlab/Hy3-295B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/r0b0tlab/Hy3-295B-NVFP4
```

Hy3-295B-NVFP4

Audited calibrated/MSE W4A4 NVFP4 v3 export of Tencent Hy3, validated with native vLLM on two NVIDIA GB10 systems.

Credits and Attribution

Base model: Tencent Hy3 authors and maintainers.
Quantization and hardware: NVIDIA ModelOpt, CUDA, and GB10.
Serving: vLLM and FlashInfer.
Evaluation tooling: Hugging Face Datasets, lm-eval, llama-benchy, and Docker.
Benchmarks: GSM8K, AI2 ARC-Challenge, PIQA, WinoGrande, TruthfulQA, MMLU, IFEval, and OpenAI HumanEval.

Please cite and follow the licenses/terms of every upstream model, library, and benchmark dataset used.

Artifact identity and quantization

Artifact: Hy3-NVFP4-w4a4-v3
Model index SHA-256: 738e3ad2c8d16372e3d681d5793c78676fd256d7a4b43d9ca560f268d26adb26
Architecture: HYV3ForCausalLM
Quantization loader: modelopt_mixed
Routed-expert backend: FLASHINFER_CUTLASS
Validated KV cache: FP8
Tensor parallelism: TP=2 across two GB10 systems
Runtime image: ghcr.io/r0b0tlab/vllm-v0250-cu130-sm121:v0.25.0-cu130-sm121-arm64-702f4814-r2
Reproducibility evidence: https://github.com/r0b0tlab/hy3-295b-nvfp4-gb10-benchmark

The artifact uses calibrated/MSE W4A4 NVFP4 for supported routed-expert projections while preserving explicitly excluded/non-quantized tensors according to the published hf_quant_config.json. No performance result in this card uses Marlin, emulation, dequantized routed experts, or BF16 routed-expert fallback.

Quality — base O0/eager, non-MTP

Task	N	Metric
gsm8k	1319	95.91%
arc_challenge	1172	96.16%
piqa	1838	94.61%
winogrande	1267	83.90%
truthfulqa_mc1	817	82.25%
mmlu_abstract_algebra	100	74.00%
mmlu_business_ethics	100	82.00%
mmlu_clinical_knowledge	265	92.83%
mmlu_college_biology	144	98.61%
mmlu_computer_security	100	88.00%
mmlu_conceptual_physics	235	94.89%
mmlu_high_school_world_history	237	93.25%
mmlu_international_law	121	92.56%
ifeval	541	91.13%
humaneval	164	84.15%

HumanEval is sandbox-executed pass@1. IFEval reports official prompt- and instruction-level strict/loose metrics in the linked structured evidence.

Performance profiles

Highest safe staged base-profile concurrency: c8.

Concurrency	Aggregate output tok/s	Per-request tok/s	Mean wall s	Mean output tokens
c1	8.73	8.73	14.66	128
c2	16.67	8.37	15.36	256
c4	32.03	8.02	15.98	512
c8	50.94	6.37	20.10	1024

MTP-1 · separate profile

MTP-1 passed at utilization 0.735; measured acceptance 52.97%; c1 mean 11.04 output tok/s under profile mtp1_o0_eager_sync. Forced 128/512/1024-token decoding passed 3/3 at each length, representative stability passed 100/100, and llama-benchy 0.4.0 completed.

The c1 label is mtp1_o0_eager_sync; it must not be presented as the base profile. MTP-1 drafted 23610 tokens and accepted 12506 (52.97%).

Practical 48K base-AR context · separate profile

The base-AR O0/eager/synchronous context profile passed a 49,152-token configured window with 49,968 measured KV-token capacity and 3,904 MiB FP8 KV. Exact near-window retrieval passed for needles near the beginning, middle, and end at up to 48,896 prompt tokens. This is a practical tested boundary, not an architectural maximum claim, and it is not an MTP result.

Reproduction and verification

Clone https://github.com/r0b0tlab/hy3-295b-nvfp4-gb10-benchmark and follow its README.md, AGENTS.md, runtime/runtime.json, sanitized scripts, structured benchmark files, and MANIFEST.sha256. Human-readable metrics are generated from the machine-readable evidence; they are not edited manually.

Notes and limitations

Quality uses only the base O0/eager/max-seqs-1 non-MTP profile.
Base concurrency, MTP-1, and base-AR context are separately launched profiles and must not be merged into one claim.
The 49,152-token context result is a practical base-AR boundary; no larger context is claimed.
NVFP4 describes model weights; the validated KV cache is FP8.
Results are specific to the documented dual-GB10 hardware, runtime image, prompts, datasets, and decoding contracts.
The benchmark suite does not establish universal model quality, safety, or optimality.
MTP performance is claimed only because preserved speculative counters prove positive drafted and accepted tokens.
Published evidence contains curated summaries, not raw private logs, telemetry streams, or per-item responses.

License

The base-model license and terms from Tencent apply to the underlying model. Dataset-specific licenses/terms apply to benchmark content. Reproduction scripts and original report assets in the linked GitHub evidence repository are released under that repository's license. Review all upstream terms before use or redistribution.

Citation

@misc{hy3,
  title={Hy3},
  author={Tencent Hy3 Team},
  year={2026},
  howpublished={Hugging Face model release}
}
@software{nvidia_modelopt,
  title={NVIDIA Model Optimizer},
  author={NVIDIA},
  url={https://github.com/NVIDIA/Model-Optimizer}
}
@software{vllm,
  title={vLLM: A High-Throughput and Memory-Efficient Inference and Serving Engine for LLMs},
  url={https://github.com/vllm-project/vllm}
}
@software{flashinfer,
  title={FlashInfer: Kernel Library for LLM Serving},
  url={https://github.com/flashinfer-ai/flashinfer}
}

Downloads last month: 708

Safetensors

Model size

170B params

Tensor type

F8_E4M3

BF16

F32

Model tree for r0b0tlab/Hy3-295B-NVFP4

Base model

tencent/Hy3

Quantized

(69)

this model