Hy3 - Sglang — ContextQMD

Deployment

For all methods and hardware platforms, see the official SGLang installation guide. The two paths below match the Python / Docker toggle in the command panel.

bash

pip install -U uv
uv venv --python 3.12 && source .venv/bin/activate

# Install from source (main carries the suffix-aware `hunyuan` parser + the
# HYV3 model code). Once a tagged release picks it up, `uv pip install sglang`
# is enough.
git clone https://github.com/sgl-project/sglang.git
cd sglang
uv pip install -e python

Run the Python output of the command panel below in that environment.

</Tab> <Tab title="Docker">

bash

# The image bundles the HYV3 model code and the suffix-aware `hunyuan` parser.
docker pull lmsysorg/sglang:dev

For how to launch the image, see Install → Method 3: Using Docker, substituting the inner sglang serve ... with what the command generator below produces.

<Note> The `dev` image bundles the HYV3 model code, the suffix-aware `hunyuan` reasoning/tool-call parsers, and the MTP draft-module runtime. The same parsers serve both the preview (suffix-less) and the shipping (suffixed) Hy3 tokenizer — no per-model hard-coding. </Note> </Tab> </Tabs> </Accordion>

Pick your hardware + recipe to generate the launch command.

Low-Latency — fastest reply for a single user. Pick for chat.
Balanced — good speed with several users at once. Use for typical multi-user serving.

import { Deployment } from "/src/snippets/_deployment.jsx"; import { config } from "/src/snippets/configs/tencent/hy3.jsx"; import { benchmarks } from "/src/snippets/configs/tencent/hy3-benchmarks.jsx";

<Deployment config={config} benchmarks={benchmarks} /> <div style={{fontSize: "0.85em", lineHeight: "1.55", color: "#6b7280", margin: "0.5rem 0 1rem 0"}}> <p style={{margin: "0 0 0.3rem 0"}}><strong>Panel controls</strong> (top of the command box):</p> <ul style={{margin: 0, paddingLeft: "1.25rem"}}> <li style={{marginBottom: "0.2rem"}}><strong>Python / Docker</strong> — bare <code>sglang serve …</code> for an existing SGLang env, or a <code>docker run … sglang serve …</code> wrap against the per-hardware image from the <a href="#install">Install SGLang</a> panel above.</li> <li style={{marginBottom: "0.2rem"}}><strong>⧉ Copy</strong> — copies the current command (with whichever framing is active) to your clipboard.</li> <li style={{marginBottom: "0.2rem"}}><strong>$ cURL</strong> — a sample request against <code>localhost:30000</code> to confirm the server is up.</li> <li style={{marginBottom: "0.2rem"}}><strong>⚙ Env</strong> — edits the placeholders (<code>HOST_IP</code>, <code>PORT</code>, <code>HF_TOKEN</code>, <code>NODE_RANK</code>, <code>NODE0_IP</code>) the command and cURL share. Persists in localStorage across cookbooks.</li> <li><strong>Verified / Not Verified</strong> badge — green when the <code>(hw, variant, quant, strategy, nodes)</code> combo has been run end-to-end on real hardware; yellow when auto-derived from a neighbor and not yet re-checked.</li> </ul> </div>

Playground

The Playground lets you turn on additional knobs on top of whichever Deploy cell is currently selected. The base is read live from your Deploy selection — only your overrides change.

The knobs come in two flavors:

Built-in SGLang features — parallelism overrides (TP / CP / DP-Attention), MoE backend + EP, reasoning / tool-call parsers, speculative-decoding presets, prefill/decode disaggregation, and HiCache tiers.
Hy3 specific — --tool-call-parser auto / --reasoning-parser auto (auto-detect Hy3's suffix-aware hunyuan parsers from the chat template; resolve the real special tokens from the tokenizer vocab at runtime).

Lines highlighted green are added by your overrides; lines with red strikethrough were in the verified base but stripped by an override. When no override differs from the base cell, the playground inherits the base's Verified badge; any actual change flips it to Not Verified until the new configuration is run end-to-end and submitted back.

import { Playground } from "/src/snippets/_playground.jsx";

<Playground config={config} /> <div style={{fontSize: "0.85em", lineHeight: "1.55", color: "#6b7280", margin: "0.5rem 0 1rem 0"}}> <p style={{margin: "0 0 0.3rem 0"}}><strong>Panel controls</strong> reuse <strong>Python / Docker</strong> · <strong>⧉ Copy</strong> · <strong>$ cURL</strong> · <strong>⚙ Env</strong> from the Deploy panel, plus one extra:</p> <ul style={{margin: 0, paddingLeft: "1.25rem"}}> <li><strong>Submit ↗</strong> — opens a pre-filled GitHub issue so you can land your override combo as a new verified cookbook cell. Shown only while the badge says <strong>Not Verified</strong>; click it once you've actually run the command on your hardware and confirmed it works.</li> </ul> </div>

1. Model Introduction

Hy3 is Tencent's third-generation flagship Mixture-of-Experts language model, featuring hybrid thinking, native tool calling, long-context reasoning, and Multi-Token Prediction (MTP) for low-latency serving.

Key Features:

MoE Architecture: 192 routed experts + 1 shared expert, top-8 activated per token. 295B total parameters with 21B active (+3.8B MTP layer), delivering dense-model quality at MoE inference cost.
Hybrid Thinking: Reasoning modes (high, low, no_think) controllable via OpenAI-standard reasoning_effort, allowing the same weights to trade off latency and depth of reasoning.
Native Tool Calling: Trained on a structured grammar. Pairs with SGLang's hunyuan tool-call parser for streaming OpenAI-compatible function-calling output.
Long Context: 256K token context window (262,144 positions) for repository-scale code and document reasoning.
Multi-Token Prediction (MTP): Ships with a built-in MTP draft module enabling speculative decoding out of the box.

Available Model:

tencent/Hy3 — BF16 instruct
tencent/Hy3-FP8 — FP8

Recommended Generation Parameters:

Special tokens. The shipping Hy3 tokenizer appends a shared suffix to every special token (e.g. <tool_calls:TAG> instead of the bare <tool_calls>). SGLang's hunyuan parsers resolve the real token strings from the tokenizer vocab at runtime (PR #29920), so the same recipe serves both the preview (suffix-less) and the shipping (suffixed) tokenizer — no per-model hard-coding. This is why --reasoning-parser hunyuan / --tool-call-parser hunyuan work out of the box on the shipping model.

2. Configuration Tips

Hardware requirements (BF16, ~590GB weights):

<table style={{width: "100%", borderCollapse: "collapse"}}> <thead> <tr style={{borderBottom: "2px solid #0052d9"}}> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.02)"}}>GPU</th> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.05)"}}>VRAM</th> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.02)"}}>TP</th> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th> </tr> </thead> <tbody> <tr> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>H200</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>141GB</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>8</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>minimum single-node for BF16</td> </tr> <tr> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>B200</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>192GB</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>BF16 590GB → 148GB/GPU</td> </tr> <tr> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>B300 / GB300</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>288GB</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>BF16 590GB → 148GB/GPU; ample KV headroom</td> </tr> <tr> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>GB200</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>192GB</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>single-node 4×192GB = 768GB fits BF16 590GB</td> </tr> </tbody> </table>

Blackwell attention backend. On SM100/SM103 (B200 / B300 / GB200 / GB300), SGLang auto-selects the trtllm_mha attention backend for HYV3's MHA architecture (no flag needed) — the launch commands above omit it for that reason. Override only if you have a specific kernel reason.

MTP (Multi-Token Prediction, EAGLE).

low-latency: steps=3, draft-tokens=4 → largest win at bs=1.
balanced: MTP disabled — keep the prefill batch moderate so chunked-prefill stays efficient.

reasoning_effort vs thinking. The Hy3 chat template is driven by reasoning_effort (high / low / no_think), NOT by the thinking flag that some other families use. The default is no_think (instant). To opt into thinking, pass reasoning_effort="high" on the request (the OpenAI-standard field; sglang forwards it to the template). reasoning_effort: max is rejected by sglang — use high. For eval, sgl-eval's --thinking flag translates to reasoning_effort="high" for Hy3, so the benchmark commands below use it as-is.

3. Advanced Usage

3.1 Reasoning (Hybrid Thinking)

Hy3 is a hybrid-thinking model. Control the thinking budget via reasoning_effort:

high / low — increasing amounts of chain-of-thought in reasoning_content
no_think — skip thinking entirely (instant responses, content-only)

Enable the reasoning parser during deployment so the thinking section is separated into reasoning_content:

bash

sglang serve \
  --model-path tencent/Hy3 \
  --tp 8 \
  --reasoning-parser auto \
  --tool-call-parser auto

</Accordion> <Accordion title="Example: thinking (reasoning_effort=high)">

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="tencent/Hy3",
    messages=[{"role": "user", "content": "Solve step by step: What is 15% of 240?"}],
    reasoning_effort="high",
    max_tokens=2048,
)

msg = response.choices[0].message
print("=============== Thinking =================")
print(msg.reasoning_content)
print("=============== Content =================")
print(msg.content)

text

=============== Thinking =================
We need to solve: "What is 15% of 240?" Step by step. 15% means 15/100 = 0.15. Multiply 0.15 by 240.
10% of 240 = 24, 5% is half of 10% = 12, so sum = 36. So answer is 36.
=============== Content =================
To find 15% of 240, follow these steps:

1. 15% = 15/100 or 0.15.
2. Multiply 240 by 0.15: 0.15 × 240 = 36.
3. Check: 10% of 240 = 24, 5% = 12, 15% = 36.

Thus, 15% of 240 is 36.

</Accordion> <Accordion title="Example: instant mode (reasoning_effort=no_think)">

python

response = client.chat.completions.create(
    model="tencent/Hy3",
    messages=[{"role": "user", "content": "Give me a one-line summary of relativity."}],
    reasoning_effort="no_think",
    max_tokens=256,
)

print("Content:", response.choices[0].message.content)

text

Content: Relativity is Einstein's theory that space, time, mass, and gravity are interconnected and relative, not fixed, fundamentally changing our understanding of the universe.

</Accordion>

3.2 Tool Calling

Hy3 supports streaming OpenAI-compatible tool calls. Enable both parsers together — the reasoning parser strips any thinking tokens before the tool-call parser runs:

bash

sglang serve \
  --model-path tencent/Hy3 \
  --tp 8 \
  --reasoning-parser auto \
  --tool-call-parser auto

</Accordion> <Accordion title="Example: non-streaming tool call">

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["city"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="tencent/Hy3",
    messages=[{"role": "user", "content": "What's the weather in Beijing? Use fahrenheit."}],
    tools=tools,
)

msg = response.choices[0].message
print("Reasoning:", msg.reasoning_content)
print("Content:  ", msg.content)
for tc in msg.tool_calls or []:
    print(f"Tool Call: {tc.function.name}")
    print(f"  Arguments: {tc.function.arguments}")

text

Reasoning: None
Content:   I'll get the current weather for Beijing in Fahrenheit for you.
Tool Call: get_weather
  Arguments: {"city": "Beijing", "unit": "fahrenheit"}

</Accordion> <Accordion title="Example: streaming tool call (incremental argument deltas)">

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

stream = client.chat.completions.create(
    model="tencent/Hy3",
    messages=[{"role": "user", "content": "What's the weather in Beijing? Use fahrenheit."}],
    tools=tools,
    stream=True,
)

tool_buffer = {}
for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.content:
        print(delta.content, end="", flush=True)
    for tc in delta.tool_calls or []:
        buf = tool_buffer.setdefault(tc.index, {"name": "", "args": ""})
        if tc.function and tc.function.name:
            buf["name"] += tc.function.name
        if tc.function and tc.function.arguments:
            buf["args"] += tc.function.arguments

for idx, buf in tool_buffer.items():
    print(f"\nTool[{idx}] {buf['name']}({buf['args']})")

text

I'll check the current weather in Beijing for you using Fahrenheit.
Tool[0] get_weather({"city": "Beijing", "unit": "fahrenheit"})

</Accordion>