Hy3

Model tree for tencent/Hy3

Finetunes

10 models

Quantizations

71 models

Spaces using tencent/Hy3 25

Collection including tencent/Hy3

[

Hy3

Collection

Hy3 • 2 items • Updated 17 days ago • 22

](https://huggingface.co/collections/tencent/hy3)

Evaluation results

• mercor/apex-agents · Apex Agents leaderboard

  25.6

• harborframework/terminal-bench-2.1 · Terminalbench 2 1 leaderboard

  71.7 *

• SWE-bench/SWE-bench_Multilingual · Swe Bench Multilingual Resolved leaderboard

  75.8

• datacurve/deep-swe · Deep Swe leaderboard

  28

• Idavidrein/gpqa · Diamond [leaderboard](https://huggingface.co/datasets/Idavidrein/gpqa?eval_result=tencent/Hy3&leade

(section continues in the model card)

License

Hy3 is released under the Apache License 2.0. See LICENSE for details.

Quantization

We provide AngelSlim, a more accessible, comprehensive, and efficient toolkit for large model compression. AngelSlim supports a comprehensive suite of compression tools for large-scale multimodal models, including common quantization algorithms, low-bit quantization, and speculative sampling.

Finetuning

Hy3 provides a complete model finetuning pipeline. For detailed documentation, please refer to: Finetuning Guide

RL Post-training

Hy3 supports GRPO reinforcement learning training with verl, training on Megatron-LM (model conversion via NVIDIA Megatron-Bridge) with vLLM rollout. For detailed documentation, please refer to: RL Training Guide

Deployment

Hy3 has 295B parameters in total. To serve it on 8 GPUs, we recommend using H20-3e or other GPUs with larger memory capacity.

For production serving, we recommend using vLLM or SGLang, both of which provide dedicated recipes for Hy3:

• vLLM - see vLLM recipes

• SGLang - see SGLang cookbook

vLLM

Build vLLM from source:

uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate
git clone https://github.com/vllm-project/vllm.git
cd vllm
uv pip install --editable . --torch-backend=auto

Start the vLLM server with MTP enabled:

## Switch to trtllm backend to work-around mnnvl workspace size issue.
export VLLM_FLASHINFER_ALLREDUCE_BACKEND=trtllm
vllm serve tencent/Hy3 \
  --tensor-parallel-size 8 \
  --speculative-config.method mtp \
  --speculative-config.num_speculative_tokens 2 \
  --tool-call-parser hy_v3 \
  --reasoning-parser hy_v3 \
  --enable-auto-tool-choice \
  --port 8000 \
  --served-model-name hy3

SGLang

Build SGLang from source:

git clone https://github.com/sgl-project/sglang
cd sglang
pip3 install pip --upgrade
pip3 install "transformers>=5.6.0"
pip3 install -e "python"

Launch SGLang server with MTP enabled:

python3 -m sglang.launch_server \
  --model tencent/Hy3 \
  --tp-size 8 \
  --tool-call-parser hunyuan \
  --reasoning-parser hunyuan \
  --speculative-num-steps 2 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 3 \
  --speculative-algorithm EAGLE \
  --port 8000 \
  --served-model-name hy3

Quickstart

Deploy Hy3 with vLLM or SGLang first, then call the OpenAI-compatible API:

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="hy3",
    messages=[
        {"role": "user", "content": "Hello! Can you briefly introduce yourself?"},
    ],
    temperature=0.9,
    top_p=1.0,
    # reasoning_effort: "no_think" (default, direct response), "low", "high" (deep chain-of-thought)
    extra_body={"chat_template_kwargs": {"reasoning_effort": "no_think"}},
)
print(response.choices[0].message.content)

Recommended parameters: temperature=0.9, top_p=1.0.

Reasoning mode: Set reasoning_effort to "high" for complex tasks (math, coding, reasoning) or "no_think" for direct responses.

See the Deployment section below for how to start the API server.

Model Links

More Reliable Product Experiences

Model usefulness is not fully captured by benchmarks. Based on extensive product feedback, we identified and fixed the following issues, receiving consistently positive feedback from product teams.

Stability of tool calls and output formats: We fixed multiple baseline reliability issues, bringing the model to production-grade standards across tool configurations and output constraints. Tool-call error recovery and overall efficiency improved. Hy3 also generalizes across different agent scaffoldings. On SWE-Bench Verified, accuracy variance across scaffoldings like CodeBuddy, Cline, and KiloCode remains within 4%.

Knowledge and anti-hallucination: Guided by the ideal of "answer when grounded, state when evidence is missing, do not conflate sources or fabricate data," we implemented fine-grained data cleaning and training constraints. In internal evaluations based on real-world scenarios, Hy3's hallucination rate dropped from 12.5% to 5.4%, and commonsense error rates fell from 25.4% to 12.7%. These improvements materially reduce fact conflation, fabrication, and logical contradiction.

Complex context retention and multi-turn intent tracking: Through joint optimization of SFT and RL, Hy3 improved on operational pain points like coreference resolution, ellipsis recovery, and multi-turn constraint inheritance. On internal comprehensive multi-turn tests, the issue rate dropped from 17.4% to 7.9%. Hy3 also improved markedly on long-dialogue evals like MRCR. Its outputs are more concise while ensuring complex intents do not decay or drift over long-horizon interactions.

Benchmark Appendix

Stronger Agent Capabilities

Building on Hy3 Preview, we further improved the quality and diversity of post-training data while scaling up RL training. Hy3 shows solid gains across reasoning, agentic, and long-context tasks, competitive with much larger flagship models.

In productivity scenarios such as coding, office work, financial modeling, frontend design, and game development, Hy3 has made remarkable progress and can now serve as a reliable, cost-effective model option.

We don't think public benchmark scores tell the full story. So we ran a blind evaluation with 270 experts using tasks from their work, and Hy3 scored 2.67/4, outperforming GLM-5.1 at 2.51/4. The advantage was most substantial in frontend development, data & storage, and CI/CD tasks.

Model Introduction

Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks.