675 lines
24 KiB
Plaintext
675 lines
24 KiB
Plaintext
---
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title: GLM-5
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metatags:
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description: "Deploy GLM-5 with SGLang on NVIDIA H100/H200/B200 and AMD MI300X/MI325X/MI355X — state-of-the-art reasoning, enhanced coding, and robust tool calling capabilities."
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---
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## 1. Model Introduction
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[GLM-5](https://huggingface.co/zai-org/GLM-5) is the most powerful language model in the GLM series developed by Zhipu AI, targeting complex systems engineering and long-horizon agentic tasks. Scaling from GLM-4.5's 355B parameters (32B active) to 744B parameters (40B active), GLM-5 integrates DeepSeek Sparse Attention (DSA) to largely reduce deployment cost while preserving long-context capacity.
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With advances in both pre-training (28.5T tokens) and post-training via [slime](https://github.com/THUDM/slime) (a novel asynchronous RL infrastructure), GLM-5 delivers significant improvements over GLM-4.7 and achieves best-in-class performance among open-source models on reasoning, coding, and agentic tasks.
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**Key Features:**
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- **Systems Engineering & Agentic Tasks**: Purpose-built for complex systems engineering and long-horizon agentic tasks
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- **State-of-the-Art Performance**: Best-in-class among open-source models on reasoning (HLE, AIME, GPQA), coding (SWE-bench, Terminal-Bench), and agentic tasks (BrowseComp, Vending Bench 2)
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- **DeepSeek Sparse Attention (DSA)**: Reduces deployment cost while preserving long-context capacity
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- **Multiple Quantizations**: BF16 and FP8 variants for different performance/memory trade-offs
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- **Speculative Decoding**: EAGLE-based speculative decoding support for lower latency
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**Available Models:**
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- **BF16 (Full precision)**: [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5)
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- **FP8 (8-bit quantized)**: [zai-org/GLM-5-FP8](https://huggingface.co/zai-org/GLM-5-FP8)
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**License:** MIT
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## 2. SGLang Installation
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Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
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## 3. Model Deployment
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This section provides deployment configurations optimized for different hardware platforms and use cases.
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### 3.1 Basic Configuration
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**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, and capabilities. SGLang supports serving GLM-5 on NVIDIA H100, H200, B200, and AMD MI300X/MI325X/MI355X GPUs.
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import { GLM5Deployment } from '/src/snippets/autoregressive/glm-5-deployment.jsx'
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<GLM5Deployment />
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<Warning>
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All recipes here run the DSA indexer top-k on the default `--dsa-topk-backend sgl-kernel`. Other top-k backend choices have not been fully validated on GLM-5.
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</Warning>
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### 3.2 Configuration Tips
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- Speculative decoding (MTP) can significantly reduce latency for interactive use cases.
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- **DP Attention**: Enables data parallel attention for higher throughput under high concurrency. Note that DP attention trades off low-concurrency latency for high-concurrency throughput — disable it if your workload is latency-sensitive with few concurrent requests.
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- The `--mem-fraction-static` flag is recommended for optimal memory utilization, adjust it based on your hardware and workload.
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- BF16 model always requires **2x GPUs** compared to FP8 on NVIDIA hardware.
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>FP8</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>BF16</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>H100</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=16</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=32</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>H200</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=16</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>B200</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=16</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MI300X/MI325X</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=8</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MI355X</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=8</td>
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</tr>
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</tbody>
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</table>
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- **B200 (FP8)**: Use `--ep 1 --attention-backend dsa --dsa-decode-backend trtllm --dsa-prefill-backend trtllm --moe-runner-backend flashinfer_trtllm --enable-flashinfer-allreduce-fusion` for optimized DSA and MoE backends on Blackwell. Also add `--quantization fp8` for FP8 weight quantization.
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- **AMD GPUs**: Use `--dsa-prefill-backend tilelang --dsa-decode-backend tilelang` for the DSA attention backend. Add `--chunked-prefill-size 131072` and `--watchdog-timeout 1200` (20 minutes for weight loading). EAGLE speculative decoding is not currently supported on AMD for GLM-5.
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- For other configuration tips (MTP, DSA kernel, Context Parallel, HiSparse, NVFP4, Index Cache), see the [DeepSeek-V3.2 cookbook page](../DeepSeek/DeepSeek-V3_2). GLM-5 and DeepSeek-V3.2 share the same model structure, so the optimization techniques are common.
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- **Prefill CP on CUDA**: Zigzag (`--cp-strategy zigzag`) is temporarily unavailable for GLM-5. Use `--enable-prefill-cp --cp-strategy interleave` with `--dp 1`.
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- Use `--json-model-override-args '{"index_topk_pattern": "FFSFSSSFSSFFFSSSFFFSFSSSSSSFFSFFSFFSSFFFFFFSFFFFFSFFSSSSSSFSFFFSFSSSFSFFSFFSSS"}'` for GLM-5-FP8 if you want to enable the [IndexCache](https://github.com/THUDM/IndexCache) method. This feature is supported through [this PR](https://github.com/sgl-project/sglang/pull/21405) and introduces only a small accuracy loss. However, if you are running rigorous accuracy evaluations, it is not recommended to enable this feature.
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## 4. Model Invocation
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Deploy GLM-5 with the following command (FP8 on H200, all features enabled):
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```shell Command
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sglang serve \
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--model-path zai-org/GLM-5-FP8 \
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--tp 8 \
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--tool-call-parser glm47 \
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--reasoning-parser glm45 \
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--speculative-algorithm EAGLE \
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--speculative-num-steps 3 \
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--speculative-eagle-topk 1 \
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--speculative-num-draft-tokens 4 \
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--enable-flashinfer-allreduce-fusion \
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--mem-fraction-static 0.85 \
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--host 0.0.0.0 \
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--port 30000
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```
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### 4.1 MI300X/MI325X/MI355X (ROCm) Server Command
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The following ROCm command is an additional option for AMD GPUs and does not replace the NVIDIA instructions above.
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```shell Command
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sglang serve \
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--model-path zai-org/GLM-5 \
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--tp 8 \
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--trust-remote-code \
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--dsa-prefill-backend tilelang \
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--dsa-decode-backend tilelang \
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--chunked-prefill-size 131072 \
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--mem-fraction-static 0.80 \
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--watchdog-timeout 1200 \
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--host 0.0.0.0 \
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--port 30000
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```
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### 4.2 Basic Usage
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For basic API usage and request examples, please refer to:
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- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
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### 4.3 Advanced Usage
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#### 4.3.1 Reasoning Parser
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GLM-5 supports Thinking mode **by default**. Enable the reasoning parser during deployment to separate the thinking and content sections. The thinking process is returned via `reasoning_content` in the streaming response.
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To disable thinking and use Instruct mode, pass `chat_template_kwargs` at request time:
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- **Thinking mode** (default): The model performs step-by-step reasoning before answering. No extra parameters needed.
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- **Instruct mode** (`{"enable_thinking": false}`): The model responds directly without a thinking process.
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**Example 1: Thinking Mode (Default)**
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Thinking mode is enabled by default. The model will reason step-by-step before answering, and the thinking process is returned via `reasoning_content`:
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:30000/v1",
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api_key="EMPTY"
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)
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# Thinking mode is enabled by default, no extra parameters needed
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response = client.chat.completions.create(
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model="zai-org/GLM-5-FP8",
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messages=[
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{"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
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],
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max_tokens=2048,
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stream=True
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)
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# Process the stream
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has_thinking = False
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has_answer = False
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thinking_started = False
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Print answer content
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if delta.content:
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# Close thinking section and add content header
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if has_thinking and not has_answer:
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print("\n=============== Content =================", flush=True)
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has_answer = True
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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The user wants me to solve a math problem: "What is 15% of 240?".
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Step 1: Understand the problem. I need to calculate a percentage of a number.
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Formula: Percentage × Number = Result.
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Step 2: Convert the percentage to a decimal or fraction.
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15% = 15/100 or 0.15.
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Step 3: Perform the multiplication.
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Method A: Decimal multiplication.
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0.15 × 240.
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Break it down:
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10% of 240 = 24.
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5% is half of 10%, so 12.
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15% = 10% + 5% = 24 + 12 = 36.
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Method B: Fraction multiplication.
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15/100 × 240.
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Simplify 240/100 = 2.4.
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15 × 2.4.
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10 × 2.4 = 24.
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5 × 2.4 = 12.
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24 + 12 = 36.
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Method C: Direct multiplication.
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240 × 0.15.
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240 × 0.10 = 24.
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240 × 0.05 = 12.
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24 + 12 = 36.
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Step 4: Final Verification.
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Is 36 reasonable?
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10% is 24. 20% is 48.
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15% is halfway between 10% and 20%.
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Halfway between 24 and 48 is 36.
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The result is correct.
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Step 5: Structure the final response. I will present the calculation clearly, perhaps showing the fractional or decimal method, or the mental math shortcut (10% + 5%).
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=============== Content =================
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Here is the step-by-step solution:
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**Step 1: Convert the percentage to a decimal.**
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To convert 15% to a decimal, divide by 100.
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$$15\% = \frac{15}{100} = 0.15$$
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**Step 2: Multiply the decimal by the number.**
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Now, multiply 0.15 by 240.
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$$0.15 \times 240$$
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**Step 3: Perform the calculation.**
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You can break this down to make it easier:
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$$0.15 = 0.10 + 0.05$$
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* First, find 10% of 240:
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$$0.10 \times 240 = 24$$
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* Next, find 5% (which is half of 10%):
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$$\frac{24}{2} = 12$$
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* Add the two results together:
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$$24 + 12 = 36$$
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**Answer:**
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15% of 240 is **36**.
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```
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**Example 2: Instruct Mode (Thinking Off)**
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To disable thinking and get a direct response, pass `{"enable_thinking": false}` via `chat_template_kwargs`:
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:30000/v1",
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api_key="EMPTY"
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)
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# Disable thinking mode via chat_template_kwargs
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response = client.chat.completions.create(
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model="zai-org/GLM-5-FP8",
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messages=[
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{"role": "user", "content": "What is 15% of 240?"}
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],
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extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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max_tokens=2048,
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stream=True
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)
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# In Instruct mode, the model responds directly without reasoning_content
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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if delta.content:
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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To find **15% of 240**, follow these steps:
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### Step 1: Convert the Percentage to a Decimal
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First, convert the percentage to a decimal by dividing by 100.
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\[
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15\% = \frac{15}{100} = 0.15
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\]
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### Step 2: Multiply by the Number
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Next, multiply the decimal by the number you want to find the percentage of.
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\[
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0.15 \times 240
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\]
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### Step 3: Perform the Multiplication
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Calculate the multiplication:
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\[
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0.15 \times 240 = 36
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\]
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### Final Answer
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\[
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\boxed{36}
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\]
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```
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#### 4.3.2 Tool Calling
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GLM-5 supports tool calling capabilities. Enable the tool call parser during deployment. Thinking mode is on by default; to disable it for tool calling requests, pass `extra_body={"chat_template_kwargs": {"enable_thinking": False}}`.
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**Python Example (with Thinking Process):**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:30000/v1",
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api_key="EMPTY"
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)
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# Define available tools
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city name"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "Temperature unit"
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}
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},
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"required": ["location"]
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}
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}
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}
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]
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# Make request with streaming to see thinking process
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response = client.chat.completions.create(
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model="zai-org/GLM-5-FP8",
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messages=[
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{"role": "user", "content": "What's the weather in Beijing?"}
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],
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tools=tools,
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stream=True
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)
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# Process streaming response
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thinking_started = False
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has_thinking = False
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Print tool calls
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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# Close thinking section if needed
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if has_thinking and thinking_started:
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print("\n=============== Content =================", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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if tool_call.function:
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print(f"Tool Call: {tool_call.function.name}")
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print(f" Arguments: {tool_call.function.arguments}")
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# Print content
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if delta.content:
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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The user is asking for the weather in Beijing. I have access to a get_weather function that can provide current weather information. Let me check what parameters are required:
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- location: required, should be "Beijing"
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- unit: optional (not in required array), can be "celsius" or "fahrenheit"
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Since the user didn't specify a unit preference and it's optional, I should not ask about it or make up a value. I'll just call the function with the required location parameter.I'll get the current weather in Beijing for you.
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=============== Content =================
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Tool Call: get_weather
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Arguments:
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Tool Call: None
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Arguments: {
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Tool Call: None
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Arguments: "location": "Be
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Tool Call: None
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Arguments: ijing"
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Tool Call: None
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Arguments: }
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```
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## 5. Benchmark
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### 5.1 Speed Benchmark
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**Test Environment:**
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- Hardware: H200 (8x)
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- Model: GLM-5-FP8
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- Tensor Parallelism: 8
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- SGLang Version: commit 947927bdb
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#### 5.1.1 Latency Benchmark
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```bash Command
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python3 -m sglang.bench_serving \
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--backend sglang \
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--model zai-org/GLM-5-FP8 \
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--dataset-name random \
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--random-input-len 1000 \
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--random-output-len 1000 \
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||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 1
|
||
Successful requests: 10
|
||
Benchmark duration (s): 35.78
|
||
Total input tokens: 6101
|
||
Total input text tokens: 6101
|
||
Total generated tokens: 4220
|
||
Total generated tokens (retokenized): 4213
|
||
Request throughput (req/s): 0.28
|
||
Input token throughput (tok/s): 170.54
|
||
Output token throughput (tok/s): 117.96
|
||
Peak output token throughput (tok/s): 148.00
|
||
Peak concurrent requests: 2
|
||
Total token throughput (tok/s): 288.50
|
||
Concurrency: 1.00
|
||
Accept length: 3.48
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 3576.31
|
||
Median E2E Latency (ms): 2935.97
|
||
P90 E2E Latency (ms): 5908.97
|
||
P99 E2E Latency (ms): 8588.08
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 290.88
|
||
Median TTFT (ms): 282.34
|
||
P99 TTFT (ms): 332.27
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 7.54
|
||
Median TPOT (ms): 6.97
|
||
P99 TPOT (ms): 9.04
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 7.80
|
||
Median ITL (ms): 6.81
|
||
P95 ITL (ms): 13.51
|
||
P99 ITL (ms): 26.99
|
||
Max ITL (ms): 29.50
|
||
==================================================
|
||
```
|
||
|
||
#### 5.1.2 Throughput Benchmark
|
||
|
||
```bash Command
|
||
python3 -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-5-FP8 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 1000 \
|
||
--max-concurrency 100 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 100
|
||
Successful requests: 1000
|
||
Benchmark duration (s): 411.74
|
||
Total input tokens: 502493
|
||
Total input text tokens: 502493
|
||
Total generated tokens: 500251
|
||
Total generated tokens (retokenized): 499614
|
||
Request throughput (req/s): 2.43
|
||
Input token throughput (tok/s): 1220.41
|
||
Output token throughput (tok/s): 1214.97
|
||
Peak output token throughput (tok/s): 2648.00
|
||
Peak concurrent requests: 105
|
||
Total token throughput (tok/s): 2435.38
|
||
Concurrency: 96.30
|
||
Accept length: 3.50
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 39648.76
|
||
Median E2E Latency (ms): 39058.12
|
||
P90 E2E Latency (ms): 57009.82
|
||
P99 E2E Latency (ms): 68880.33
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 20613.80
|
||
Median TTFT (ms): 21429.21
|
||
P99 TTFT (ms): 29543.17
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 38.73
|
||
Median TPOT (ms): 36.52
|
||
P99 TPOT (ms): 67.09
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 38.13
|
||
Median ITL (ms): 16.57
|
||
P95 ITL (ms): 86.01
|
||
P99 ITL (ms): 164.88
|
||
Max ITL (ms): 1307.02
|
||
==================================================
|
||
```
|
||
|
||
### 5.2 Accuracy Benchmark
|
||
|
||
<Note>
|
||
The accuracy benchmark results below are shared with GLM-5.1, as GLM-5.1 was not independently benchmarked at the time of this writing. A separate GLM-5.1 benchmark run is planned.
|
||
</Note>
|
||
|
||
#### 5.2.1 GSM8K Benchmark
|
||
|
||
- Benchmark Command
|
||
```bash Command
|
||
python3 benchmark/gsm8k/bench_sglang.py --port 30000
|
||
```
|
||
|
||
- Test Result
|
||
```text Output
|
||
Accuracy: 0.955
|
||
Invalid: 0.000
|
||
Latency: 32.470 s
|
||
Output throughput: 642.044 token/s
|
||
```
|
||
|
||
#### 5.2.2 MMLU Benchmark
|
||
|
||
- Benchmark Command
|
||
```bash Command
|
||
python3 benchmark/mmlu/bench_sglang.py --port 30000
|
||
```
|
||
|
||
- Test Result
|
||
```text Output
|
||
subject: abstract_algebra, #q:100, acc: 0.860
|
||
subject: anatomy, #q:135, acc: 0.874
|
||
subject: astronomy, #q:152, acc: 0.941
|
||
subject: business_ethics, #q:100, acc: 0.880
|
||
subject: clinical_knowledge, #q:265, acc: 0.932
|
||
subject: college_biology, #q:144, acc: 0.972
|
||
subject: college_chemistry, #q:100, acc: 0.640
|
||
subject: college_computer_science, #q:100, acc: 0.900
|
||
subject: college_mathematics, #q:100, acc: 0.810
|
||
subject: college_medicine, #q:173, acc: 0.873
|
||
subject: college_physics, #q:102, acc: 0.912
|
||
subject: computer_security, #q:100, acc: 0.880
|
||
subject: conceptual_physics, #q:235, acc: 0.928
|
||
subject: econometrics, #q:114, acc: 0.807
|
||
subject: electrical_engineering, #q:145, acc: 0.897
|
||
subject: elementary_mathematics, #q:378, acc: 0.937
|
||
subject: formal_logic, #q:126, acc: 0.778
|
||
subject: global_facts, #q:100, acc: 0.710
|
||
subject: high_school_biology, #q:310, acc: 0.961
|
||
subject: high_school_chemistry, #q:203, acc: 0.847
|
||
subject: high_school_computer_science, #q:100, acc: 0.960
|
||
subject: high_school_european_history, #q:165, acc: 0.891
|
||
subject: high_school_geography, #q:198, acc: 0.960
|
||
subject: high_school_government_and_politics, #q:193, acc: 0.984
|
||
subject: high_school_macroeconomics, #q:390, acc: 0.923
|
||
subject: high_school_mathematics, #q:270, acc: 0.696
|
||
subject: high_school_microeconomics, #q:238, acc: 0.962
|
||
subject: high_school_physics, #q:151, acc: 0.821
|
||
subject: high_school_psychology, #q:545, acc: 0.956
|
||
subject: high_school_statistics, #q:216, acc: 0.889
|
||
subject: high_school_us_history, #q:204, acc: 0.941
|
||
subject: high_school_world_history, #q:237, acc: 0.945
|
||
subject: human_aging, #q:223, acc: 0.857
|
||
subject: human_sexuality, #q:131, acc: 0.908
|
||
subject: international_law, #q:121, acc: 0.934
|
||
subject: jurisprudence, #q:108, acc: 0.907
|
||
subject: logical_fallacies, #q:163, acc: 0.933
|
||
subject: machine_learning, #q:112, acc: 0.830
|
||
subject: management, #q:103, acc: 0.942
|
||
subject: marketing, #q:234, acc: 0.940
|
||
subject: medical_genetics, #q:100, acc: 0.990
|
||
subject: miscellaneous, #q:783, acc: 0.959
|
||
subject: moral_disputes, #q:346, acc: 0.873
|
||
subject: moral_scenarios, #q:895, acc: 0.837
|
||
subject: nutrition, #q:306, acc: 0.922
|
||
subject: philosophy, #q:311, acc: 0.897
|
||
subject: prehistory, #q:324, acc: 0.929
|
||
subject: professional_accounting, #q:282, acc: 0.844
|
||
subject: professional_law, #q:1534, acc: 0.714
|
||
subject: professional_medicine, #q:272, acc: 0.941
|
||
subject: professional_psychology, #q:612, acc: 0.913
|
||
subject: public_relations, #q:110, acc: 0.791
|
||
subject: security_studies, #q:245, acc: 0.878
|
||
subject: sociology, #q:201, acc: 0.940
|
||
subject: us_foreign_policy, #q:100, acc: 0.920
|
||
subject: virology, #q:166, acc: 0.596
|
||
subject: world_religions, #q:171, acc: 0.936
|
||
Total latency: 165.275
|
||
Average accuracy: 0.877
|
||
```
|
||
|
||
### 5.3 AMD GPU Benchmarks
|
||
|
||
#### 5.3.1 GSM8K Benchmark (MI325/MI35x)
|
||
|
||
- MI325/MI35x Test (GLM-5 BF16, `tp=8`, TileLang DSA backends)
|
||
|
||
```bash Command
|
||
python3 benchmark/gsm8k/bench_sglang.py --num-questions 200
|
||
```
|
||
|
||
```text Output
|
||
Accuracy: 0.970
|
||
Invalid: 0.000
|
||
```
|
||
|
||
Results from [AMD nightly CI](https://github.com/sgl-project/sglang/actions/runs/22556197510/attempts/2#summary-65346783629). See also [sglang#18911](https://github.com/sgl-project/sglang/pull/18911).
|