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Nitro-1-SD
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View details ↗tiny-llama-fast-tokenizer
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View details ↗tiny-random-qwen3_moe
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View details ↗The model was quantized from Qwen/Qwen3.5-397B-A17B-FP8 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers. The GSM8K results were obtained using the vLLM framework, based on the Docker image `rocm/vllm-dev:nightly_main_20260211`, and vLLM is installed inside the container.
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View details ↗This checkpoint extends the routed-expert MXFP4 quantization by **also quantizing the shared expert to MXFP4 and fusing it into the routed MoE kernel** (FSE: fused shared expert). Compared with keeping the shared expert in bf16, this further reduces the bf16 footprint and improves decode throughput, with no measurable accuracy loss on GSM8K (see Evaluation). The model was quantized from Qwen/Qwen3.5-397B-A17B using AMD-Quark. Weights and activations are quantized to OCP MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
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View details ↗- **Model Architecture:** Qwen3_5MoeForCausalLM - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350 / MI355 - **ROCm**: 7.2.0 - **PyTorch**: 2.12.0 - **Transformers**: 5.14.1 - **Operating System(s):** Linux - **Inference Engine:** SGLang/vLLM - **Model Optimizer:** AMD-Quark (v0.12) - **Quantized layers:** router experts - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic The model was quantized from Qwen/Qwen3.8-2.4T-A95B-FP8 using AMD-Quark. The weights and activations of the routed experts are quantized to MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
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View details ↗INT4 weight-only (W4A16) quantized version of `Qwen/Qwen3.8-27B`, produced with the **AWQ** algorithm using AMD Quark. \* BFCL Overall Acc reflects only `single_turn` categories, not the full Gorilla-leaderboard formula (multi-turn/web-search/memory categories were not run and would count as 0 against the public leaderboard's own Overall Acc). Sub-metrics: Non-Live AST 86.58% (base 88.52%), Live AST 81.57% (base 83.05%), Relevance Detection 75.00% (base 75.00%), Irrelevance Detection 72.47% (base 72.22%). Recovery % = quantized / BF16-base, using flexible-extract for GSM8K rows and base/quantized (inverted, since lower is better) for perplexity — both measured by us against verified-upstream `Qwen/Qwen3.8-27B` weights, not vendor-reported numbers. GSM8K uses `lm-evaluation-harness`; non-thinking mode is approximated by pre-closing an empty ` ` block in the prompt, since the harness task is a raw few-shot completion rather than a chat-templated request. BFCL run via the official `bfcl_eval` harness.
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View details ↗- **Weights:** MXFP4 (OCP microscaling FP4, group size 32, E8M0 shared scale) - **Activations:** MXFP4, dynamic (per-block, computed at runtime) - **Algorithm:** AWQ - **Calibration:** 128 samples, seq_len 512, `pileval` dataset (Quark default) - **Base model:** Qwen/Qwen3.8-27B (bf16) - **Vision tower:** left unquantized (bf16) — only the language-model decoder (`model.language_model.layers`) is quantized; `model.visual.*` is excluded Recovery % is computed on the flexible-extract metric (`this model / bf16 base`). Non-thinking mode: the raw-completions endpoint used above doesn't support passing `enable_thinking=False` through a chat template, so non-thinking mode is approximated with a custom task that pre-closes an empty ` ` block in the prompt:
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View details ↗This checkpoint quantizes the routed **and** shared MoE experts to OCP MXFP4 and keeps everything else in bf16, including the full self-attention and the (Mamba-style) linear-attention projections, the MTP head, router gates, shared-expert gate, PLE n-gram path, vision tower, hyper-connection mixers, and `lm_head`. The model was quantized from Qwen3.8-Flash-Next using AMD-Quark. MoE expert weights and activations are quantized to OCP MXFP4. The GSM8K results were obtained using the vLLM framework, based on the Docker image `vllm/vllm-openai-rocm:v0.32.0`, with lm-eval installed from source. This Reproduction serves the model without MTP. Enabling MTP on the formal `v0.32.0` release currently requires vLLM PR #61770.
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