Roy WangandClaude Opus 5.5
b515648641
Signed-off-by: esmeetu <jasonailu87@gmail.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
141 lines
3.4 KiB
YAML
141 lines
3.4 KiB
YAML
meta:
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title: "Qwen3.5-2B"
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slug: "qwen3.5-2b"
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provider: "Qwen"
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description: "Qwen3.5 mini dense multimodal model (2B) — edge / low-VRAM serving with 262K context"
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date_added: 2026-04-22
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date_updated: 2026-09-14
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tasks:
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- multimodal
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- text
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performance_headline: "Edge-scale Qwen3.5 dense — fits on 8 GB GPUs or 1x Intel Arc Pro B60/B70"
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related_recipes:
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- "Qwen/Qwen3.5-4B"
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- "Qwen/Qwen3.5-0.8B"
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hardware:
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arc_pro_b60: verified
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arc_pro_b70: verified
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model:
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model_id: "Qwen/Qwen3.5-2B"
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min_vllm_version: "0.17.0"
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default_frontend: rust
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architecture: dense
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parameter_count: "2B"
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active_parameters: "2B"
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context_length: 262144
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base_args:
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- "--trust-remote-code"
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base_env: {}
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features:
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tool_calling:
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description: "Enable automatic tool choice with Qwen3 Coder parser"
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args:
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- "--enable-auto-tool-choice"
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- "--tool-call-parser"
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- "qwen3_coder"
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reasoning:
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description: "Enable chain-of-thought reasoning with Qwen3 parser"
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args:
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- "--reasoning-parser"
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- "qwen3"
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spec_decoding:
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description: "Multi-token prediction speculative decoding for lower latency"
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args:
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- "--speculative-config"
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- '{"method":"mtp","num_speculative_tokens":1}'
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text_only:
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description: "Skip loading the vision encoder for text-only workloads."
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args:
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- "--language-model-only"
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opt_in_features:
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- spec_decoding
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- text_only
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variants:
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default:
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precision: bf16
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vram_minimum_gb: 5
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description: "Full precision BF16 — fits on an 8 GB GPU or Intel Arc Pro B60/B70"
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compatible_strategies:
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- single_node_tp
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hardware_overrides:
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xpu:
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extra_args:
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- "--enforce-eager"
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strategy_overrides: {}
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guide: |
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## Overview
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[Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) is a miniature dense
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Qwen3.5 model — the full gated delta networks architecture, vision encoder,
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and 262K context, in a form small enough for 8 GB consumer GPUs or edge
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inference.
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## Prerequisites
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- **vLLM version:** >= 0.17.0
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- **Hardware:** single 8 GB GPU or Intel Arc Pro B60/B70
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### Install vLLM
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```bash
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uv venv
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source .venv/bin/activate
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uv pip install -U vllm --torch-backend=auto
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```
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### Docker
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```bash
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docker pull vllm/vllm-openai-xpu:latest # Intel XPU (B60 / B70)
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```
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## Launching the Server
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```bash
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vllm serve Qwen/Qwen3.5-2B \
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--max-model-len 262144 \
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--reasoning-parser qwen3
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```
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### Docker (Intel XPU B60 / B70)
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Validated on 1× Intel Arc Pro B60 / B70 (B60 24 GB, B70 32 GB per card) with the
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official vLLM XPU image `vllm/vllm-openai-xpu:latest`.
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```bash
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docker run --device /dev/dri \
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-v /dev/dri/by-path:/dev/dri/by-path --shm-size=16g \
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--privileged --ipc=host -p 8000:8000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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vllm/vllm-openai-xpu:latest \
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Qwen/Qwen3.5-2B \
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--reasoning-parser qwen3 \
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--enforce-eager
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```
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## Client Usage
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```python
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from openai import OpenAI
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client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
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resp = client.chat.completions.create(
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model="Qwen/Qwen3.5-2B",
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messages=[{"role": "user", "content": "Hi!"}],
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max_tokens=64,
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)
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print(resp.choices[0].message.content)
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```
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## References
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- [Model card](https://huggingface.co/Qwen/Qwen3.5-2B)
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- [Base checkpoint](https://huggingface.co/Qwen/Qwen3.5-2B-Base)
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