Qwable-v1-NVFP4A16

Qwable-v1-NVFP4A16

NVFP4 quantization of lordx64/Qwable-v1 — a 35B-total / 3B-active text generation Mixture-of-Experts model (Qwen3_5MoeForConditionalGeneration, Qwen3.6 family, with hybrid linear / full attention). Per the base model card it is text-only and aimed at reasoning, agentic tool-use, and coding (see Capabilities).

Variant: NVFP4 weight-only (W4A16) — 4-bit float weights, group size 16, per-group FP8 (e4m3) scales + per-tensor FP32 global scales; activations stay BF16 Disk size: ~24 GB (vs ~67 GB BF16, ~2.8×) Quantized by: sahilchachra Tooling: llm-compressor model_free_ptq (data-free, streaming PTQ — no calibration data)

Note on what is quantized: only the linear weights that hold the bulk of the parameters are taken to NVFP4 — the 256-way routed experts, the shared experts, and the full-attention projections. The linear/Gated-Delta-Net (mamba-style) layers, the MoE routers, embeddings, lm_head, the MTP head and all norms are kept in BF16 for stability. The architecture also carries a vision tower (Qwen3_5MoeForConditionalGeneration), which is likewise kept in BF16 — but the base model is documented as text-only, so this quantization neither adds nor validates any image capability. The headline variant name reflects the dominant (expert/attention) quantization; the on-disk size averages the NVFP4 and BF16 halves of the model.

Capabilities

Unchanged from the base model — quantization only changes weight precision, not behavior. Per the base model card:

• Reasoning — thinks in explicit <think>…</think> chains-of-thought.
• Agentic tool-use — emits <tool_use> XML blocks for file/shell operations (activates with agent-style system prompts or prior <tool_result> turns).
• Coding — designed for agentic coding tasks with multi-turn agent interactions.
• Context length: 4096 tokens (training) / 16384 tokens (serving).

See the base card for limitations (narrow training distribution, tool-name differences, reasoning inherited from the Opus-4.7 distill).

Smoke test

Loaded and run with vLLM 0.19 on an NVIDIA Thor (Blackwell) device. The model loads, captures CUDA graphs, runs the hybrid linear-attention + NVFP4 MoE path, and produces coherent text. This is a functional smoke test only — it is not a quality benchmark.

Generation speed

Quick on-device measurement (not a tuned benchmark): warmed, short chat-templated prompt, greedy decoding, CUDA graphs enabled, identical settings for both variants, single GPU.

Single-stream decode is memory-bandwidth bound, so the ~4× smaller weights give the largest gain (~1.4×); batched decode is more compute-bound and the W4A16 dequant cost narrows the gap. Numbers will vary with prompt length, batch size and KV-cache growth (this is a reasoning model — long thinking traces decode more tokens).

Test device

• GPU: NVIDIA Thor (Blackwell, native NVFP4)
• CPU / memory: 14-core ARM (aarch64), 122 GB unified memory
• Software: JetPack / L4T R38.4 (Ubuntu 24.04), CUDA 13.0, driver 580, kernel 6.8.12-tegra
• Serving: vLLM 0.19 (ghcr.io/nvidia-ai-iot/vllm:latest-jetson-thor)

What's quantized

Usage (vLLM)

from vllm import LLM, SamplingParams

llm = LLM(model="sahilchachra/Qwable-v1-NVFP4A16", dtype="bfloat16", max_model_len=16384)
out = llm.generate(["Hello!"], SamplingParams(temperature=0.0, max_tokens=128))
print(out[0].outputs[0].text)

Runs on Blackwell GPUs with native NVFP4 support.

Notes

• Weight-only NVFP4 (W4A16): weights are 4-bit, activations remain BF16.
• Format: nvfp4-pack-quantized (compressed-tensors), per-expert layout — the standard layout vLLM consumes for quantized MoE.
• Smoke-tested only; not formally benchmarked for quality.

Original model

See lordx64/Qwable-v1 for full lineage, intended use, and limitations. License (AGPL-3.0) is inherited from the base model.