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NVFP4 self-quant (llm-compressor): FP8 attn/GDN + NVFP4-W4A16 experts; beats redhat/unsloth on quality+speed+size

Browse files
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model: Qwen/Qwen3.6-35B-A3B
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+ base_model_relation: quantized
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+ tags:
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+ - nvfp4
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+ - fp4
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+ - llm-compressor
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+ - compressed-tensors
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+ - vllm
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+ - moe
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+ - qwen3_5_moe
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # Qwen3.6-35B-A3B-NVFP4 (self-quantized, llm-compressor)
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+
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+ NVFP4 (4-bit) quantization of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B), the hybrid
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+ **Gated-DeltaNet + 256-expert MoE** model (35B total / 3B active, multimodal, thinking-by-default).
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+ Produced in-house with **llm-compressor / compressed-tensors**, tuned for **NVIDIA Blackwell (sm120, RTX PRO 6000)**.
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+
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+ **22.5 GB** (≈3× smaller than the 67 GB BF16 base) — the **smallest** of the public NVFP4 builds, while matching or
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+ beating them on quality and beating two of three on speed.
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+
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+ ## Recipe (mixed-precision)
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+
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+ | Component | Precision |
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+ |---|---|
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+ | Routed experts (256/layer, fused) | **NVFP4 weight-only (W4A16)** group-16 |
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+ | Self-attention q/k/v/o + GDN `in_proj_*`/`out_proj` + shared-expert | **FP8** (W8A8, block-128 weight / dynamic group-128 act) |
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+ | Routers, `lm_head`, embeddings, conv1d/SSM, vision tower, MTP | **BF16** |
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+
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+ Why weight-only NVFP4 on experts: on sm120 the native FP4 MoE GEMM is unavailable, so all NVFP4 experts serve via the
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+ **Marlin W4A16** path regardless — W4A16 therefore gives the same speed as W4A4 with less quantization error.
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+ Calibrated with `moe_calibrate_all_experts=True` (every one of the 256 experts receives stats).
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+
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+ ## Benchmarks (measured on RTX PRO 6000 / sm120, vLLM 0.23)
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+
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+ lm-eval (thinking-on, `max_gen_toks=8192`, flexible-extract); speed from engine `/metrics`, TP1 solo.
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+
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+ | Build | MMLU-Pro | GSM8K | single-stream tok/s | N16 tok/s | size |
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+ |---|---|---|---|---|---|
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+ | **this model (our self-quant)** | **0.825** | **0.920** | 200.6 | 1581 | **22.5 GB** |
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+ | unsloth/…-NVFP4 | 0.825 | 0.890 | 175.3 | 1493 | 24.7 GB |
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+ | RedHatAI/…-NVFP4 | 0.819 | 0.910 | 170.0 | 1422 | 24.0 GB |
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+ | nvidia/…-NVFP4 | 0.817 | 0.910 | **223.6** | **1646** | 23.4 GB |
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+
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+ ![benchmark](benchmark.png)
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+
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+ - **Pareto-dominates** RedHatAI & unsloth on quality, speed, *and* size.
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+ - **Tied-best quality** (top GSM8K, tied-top MMLU-Pro); **smallest** build.
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+ - nvidia keeps the single-stream/concurrent speed crown (it sits on the sm120 hardware optimum — FP8-attn + W4A16-experts via Marlin); this build matches its scheme and trails only on raw decode throughput.
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+
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+ ## Serving (vLLM ≥ 0.23)
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+
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+ ```bash
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+ vllm serve <this-repo> --served-model-name qwen3.6-35b-a3b-nvfp4 \
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+ --max-model-len 262144 --gpu-memory-utilization 0.90 \
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+ --trust-remote-code --reasoning-parser qwen3 \
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+ --enable-auto-tool-choice --tool-call-parser qwen3_xml
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+ ```
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+
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+ Quantized by `kyaky` with llm-compressor. Base model © Qwen, Apache-2.0.
benchmark.png ADDED
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+ {%- set image_count = namespace(value=0) %}
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+ {%- set video_count = namespace(value=0) %}
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+ {%- macro render_content(content, do_vision_count, is_system_content=false) %}
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+ {%- if content is string %}
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+ {{- content }}
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+ {%- if is_system_content %}
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+ {{- raise_exception('System message cannot contain images.') }}
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+ {%- endif %}
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+ {%- if do_vision_count %}
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+ {%- set image_count.value = image_count.value + 1 %}
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+ {%- endif %}
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+ {%- if add_vision_id %}
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+ {{- 'Picture ' ~ image_count.value ~ ': ' }}
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+ {%- endif %}
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+ {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
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+ {%- elif 'video' in item or item.type == 'video' %}
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+ {%- if is_system_content %}
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+ {{- raise_exception('System message cannot contain videos.') }}
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+ {%- if do_vision_count %}
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+ {%- set video_count.value = video_count.value + 1 %}
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+ {%- endif %}
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+ {%- if add_vision_id %}
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+ {{- 'Video ' ~ video_count.value ~ ': ' }}
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+ {%- endif %}
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+ {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
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+ {%- elif 'text' in item %}
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+ {{- item.text }}
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+ {%- else %}
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+ {{- raise_exception('Unexpected item type in content.') }}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- elif content is none or content is undefined %}
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+ {{- '' }}
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+ {%- else %}
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+ {%- endmacro %}
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+ {{- raise_exception('No messages provided.') }}
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+ {%- if tools and tools is iterable and tools is not mapping %}
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+ {{- '<|im_start|>system\n' }}
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+ {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\n</tools>" }}
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+ {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" %}
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+ {%- set ns.multi_step_tool = false %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if ns.multi_step_tool %}
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+ {{- raise_exception('No user query found in messages.') }}
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+ {%- endif %}
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+ {{- raise_exception('System message must be at the beginning.') }}
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+ {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set reasoning_content = message.reasoning_content %}
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+ {%- if '</think>' in content %}
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+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
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+ {{- '<|im_start|>assistant\n' }}
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+ {{- '<think>\n\n</think>\n\n' }}
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+ quant_stage:
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+ quant_modifiers:
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+ QuantizationModifier:
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+ group_0:
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+ targets: ['re:.*self_attn.q_proj.*', 're:.*self_attn.k_proj.*', 're:.*self_attn.v_proj.*',
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+ weights:
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13
+ ],
14
+ "image_std": [
15
+ 0.5,
16
+ 0.5,
17
+ 0.5
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+ ],
19
+ "processor_class": "Qwen3VLProcessor",
20
+ "video_processor_type": "Qwen3VLVideoProcessor"
21
+ }