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greghavens/
fabletron-nemotron-3-super-120b-NVFP4

Name: fabletron-nemotron-3-super-120b-NVFP4
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Fabletron — Nemotron-3-Super-120B-A12B · Fable-5 (NVFP4)

QLoRA fine-tune of NVIDIA Nemotron-3-Super-120B-A12B (120B-total / 12B-active hybrid Mamba-2 + Latent-MoE, nemotron_h) on the pi_agent split of Glint-Research/Fable-5-traces, targeting reasoning, agentic planning, and tool-use. This card documents the NVFP4 export (NVIDIA ModelOpt format) for vLLM / TensorRT-LLM on NVIDIA Blackwell (FP4 tensor cores).

Status: the ≈67 GB NVFP4 weights (7 safetensors shards) are uploaded here and the export loads and serves on vLLM on an RTX PRO 6000 Blackwell Workstation (sm_120) at the full native 262144-token context. Measured server-side throughput at this precision: ≈63.7 tok/s per-request decode, ≈430 tok/s aggregate decode (concurrent sequences), ≈890 tok/s prefill. See the exact, verified serving recipe below. Siblings: the most-universal GGUF Q4_K_M and the pre-merge LoRA adapter.

Why ModelOpt and not compressed-tensors? vLLM only accepts unquantized, ModelOpt FP8, or ModelOpt NVFP4 checkpoints for nemotron_h's non-gated MoE (is_act_and_mul=False); compressed-tensors NVFP4 is rejected for this architecture (vLLM #31782). This export is therefore produced with NVIDIA ModelOpt and served with -q modelopt_fp4.

LM Studio download note: there is a known LM Studio bug where its HF-proxy downloader hangs at "Finalizing download…" on empty (0-byte) files and never marks the model ready — tracked upstream at lmstudio-ai/lmstudio-bug-tracker#1477. This is a vLLM / TensorRT-LLM export, not an LM Studio format (use the MLX-4bit or GGUF siblings for LM Studio) — but the empty __init__.py that ships with nemotron_h repos triggers that bug for anyone pulling this repo through LM Studio's downloader. As a precaution it now ships a non-empty __init__.py so such downloads finalize normally.

Serving (vLLM, verified recipe)

This is the exact recipe used in production on sm_120 (RTX PRO 6000 Blackwell) — CUDA graphs on (no --enforce-eager), FlashInfer autotune on, native FP4 MoE:

bash

# The FP8 Mamba in/out_proj GEMM: FlashInfer's cuDNN per-tensor-q GRAPH plan fails to
# build on sm_120 during CUDA-graph capture. Disable ONLY that one FP8 kernel so it falls
# back to CutlassFP8ScaledMMLinearKernel (graph-capturable). The FP4 experts/MoE stay
# 100% native FlashInfer CUTLASS -> no --enforce-eager needed.
export VLLM_DISABLED_KERNELS="FlashInferFP8ScaledMMLinearKernel"

vllm serve greghavens/fabletron-nemotron-3-super-120b-NVFP4 \
  -q modelopt_fp4 \
  --trust-remote-code \
  --mamba-ssm-cache-dtype float32 \
  --kv-cache-dtype auto \
  --max-model-len 262144 \
  --max-num-seqs 16 \
  --gpu-memory-utilization 0.90 \
  --enable-chunked-prefill \
  --reasoning-parser nemotron_v3 \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --served-model-name nemotron-3-super-fable5-nvfp4 nemotron

Pass -q modelopt_fp4 explicitly: the checkpoint's quant_method may be empty and would otherwise be missed (ModelOpt #261).

Context: only 8 of 88 layers are attention, so KV is ≈8 KiB/token (≈2 GiB/seq at full 262144). Serving at a short context truncates BFCL multi-turn and MMLU-Pro 5-shot CoT — the native 262144 guarantees nothing truncates.

Reasoning/tools: the model emits <think>…</think>; --reasoning-parser nemotron_v3 routes that into reasoning_content so the graded content is the post-think answer. --tool-call-parser qwen3_coder handles OpenAI-style tool_calls. Uses the bundled chat_template.jinja.

The base model's MTP (next-n-predict) head is not carried into this export (ModelOpt #750).

Quantization

Format: NVIDIA ModelOpt MIXED_PRECISION — NVFP4 (4-bit float, per-block 16-wide, FP8 scales, dynamic FP4 activations) for most matmuls, FP8 for the Mamba projections. Calibration algorithm: max. (Verified from the export's hf_quant_config.json.)
NVFP4 (W4A4): attention q/k/v/o_proj (32 tensors); all 512 routed + shared expert up/down_proj (41,040 tensors); MoE latent fc1/fc2_latent_proj (80 tensors).
FP8 (W8A8): Mamba-2 in_proj/out_proj (80 tensors) — kept at higher precision.
Kept in BF16 (precision-sensitive / unsupported for FP4): the MoE router (mixer.gate), Mamba-2 conv1d, lm_head, token embeddings, all RMSNorms, and the MTP head.
Calibration: 512 samples from the exact training distribution (data/train.jsonl, ChatML), sequence length 2048.
Precision note: this export follows NVIDIA's conservative recipe — Mamba in_proj/out_proj are kept at FP8 (an earlier all-NVFP4 attempt was discarded as nvfp4.uniform-broken). For maximum fidelity prefer the GGUF Q4_K_M sibling repo.

Training

Method: QLoRA (4-bit NF4 base) via Unsloth. LoRA r=8, α=16, dropout=0, bias=none. Targets (by regex) attention q/k/v/o_proj, Mamba-2 in_proj/out_proj, and the up/down_proj of all 512 routed experts + the shared expert — this MoE is non-gated, so up/down_proj is the full expert FFN (there is no gate_proj). The MoE router (mixer.gate, an nn.Parameter) and the MoE-latent fc1/fc2_latent_proj stay frozen.
Data: Glint-Research/Fable-5-traces, config pi_agent, ChatML, response-only loss.
Schedule: 1 epoch (248 steps), grad-accum 16, lr 2e-5 cosine + warmup.
Final training loss: ≈0.74 true per-token CE (final-phase mean; Unsloth logs loss × grad-accum, so the raw logged value is ÷16 here).
Evaluation (held-out Fable-5 pi_agent, 81 rows, base → fine-tuned): cross-entropy 1.12 → 0.78, perplexity 3.07 → 2.18.
Date: 2026-06-29.
LoRA adapter (pre-merge) + other formats: greghavens/fabletron-nemotron-3-super-120b.

Provenance & license

This is a derivative of two upstream works, and downstream use must comply with both:

Base model: NVIDIA Nemotron-3-Super-120B-A12B — see NVIDIA's model license.
Dataset: Glint-Research/Fable-5-traces — AGPL-3.0, and distilled from Anthropic Claude outputs (subject to Anthropic's usage terms).

Released as a research artifact. Review the upstream licenses before redistribution or production use. license: other reflects the combination of the above, not a single license.

Intended use

Research on hybrid Mamba-2/MoE reasoning models, agentic/tool-use experimentation. Not safety-aligned beyond the base model; evaluate before any production deployment.

Benchmarks

Fabletron is a Fable-5 fine-tune of Nemotron-3-Super-120B-A12B. This repository hosts the NVFP4 (for vLLM on NVIDIA Blackwell) build. The results below were produced through a deployed NeMo Evaluator at full context length (262 144) with a 32 768-token generation budget, comparing base Nemotron-3-Super against the Fabletron fine-tune in its two served formats — GGUF (llama.cpp) and NVFP4 (vLLM / Blackwell).

Accuracy

Benchmark	Base Nemotron 3 Super	Fabletron (GGUF)	Fabletron (NVFP4)
IFEval	89.6	90.4	87.4
GSM8K	96.6	96.5	96.5
MMLU-Pro	81.1	81.5	81.2
HumanEval+	87.8	89.6	89.6
MBPP+	95.5	96.6	96.8
BFCL (v3 · multi_turn + ast)	31.7	53.6	53.9

Scores are % (higher is better). The standout is function-calling (BFCL): 31.7 → 53.6 — base Nemotron largely fails multi-turn and parallel tool use, which the fine-tune resolves (single-tool calls 0.8 → 85.7, multi-turn 0 → 45). General reasoning and coding hold or improve slightly, and NVFP4 tracks the GGUF fine-tune within noise — the only give-back to quantization is IFEval strict (≈2 points).

Throughput

Mean per-request decode rate across the 6 benchmark families (tok/s, client-side, parallelism 4): base 45.7 · Fabletron GGUF 46.3 · Fabletron NVFP4 63.8. The NVFP4 build serves at ≈1.4× the GGUF (llama.cpp) rate on an RTX PRO 6000 (Blackwell).

_{BFCL = Berkeley Function-Calling Leaderboard v3 (multi_turn,ast, native tool-calling, via nvidia_bfcl 26.3). Base and GGUF share the llama.cpp engine; NVFP4 runs on vLLM. Output truncation at this budget is ≈0 across families (MMLU-Pro shows ≈3.5% length-capped generations, intrinsic to the benchmark and near-identical for all three models, so it does not bias the comparison).}