--- library_name: transformers pipeline_tag: text-generation license: apache-2.0 base_model: - tsinghua-sigs-robot-lab/VeriLoop-E2 base_model_relation: quantized language: - en - zh tags: - veriloop - veriloop-coder - code - coding-agent - software-engineering - mathematical-reasoning - nvfp4 - modelopt - tensorrt-llm - vllm - sglang - code-optimized - quantization - open-source - apache-2.0 - qwen3_5 - self-harness - harness-engineering - surface-host-adapter - evidence-binding - rollback - uncertainty-calibration - long-context - vertical-code-model - recursive-improvement ---

VeriLoop Coder E2 · NVFP4

Coding-Optimized Quantized Model (NVIDIA NVFP4)

Original Model ↗ · GitHub · Apache-2.0

--- ## Overview This repository contains an **NVFP4 quantization** of [VeriLoop E2](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2), an open 27B post-trained model built on **Qwen3.8-27B** for code, mathematics, and physics. Its core reasoning discipline is **VeriLoop-Governed Recurrence (VGR)**: candidate states are recursively proposed, externally checked, and retained only when the protected evidence state improves without regression. Quantized by [Rodrigo Ramos](https://github.com/rodrigoramosrs). ## Quantization Approach Produced with [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer) using the canonical **`NVFP4_W4A4_WEIGHT_LOCAL_HESSIAN_CFG`** recipe: static per-block 4-bit weights (group size 16) + dynamic 4-bit activations, FP8 attention, local-Hessian calibration with MSE and fp8 scale sweep. The `linear_attn` (Gated Delta Net) blocks and the self-attention projections ship in BF16 on purpose — matching validated official NVFP4 releases for this architecture family (which exclude `linear_attn*` per layer) and the widely-deployed MLP-only NVFP4 pattern. Only the MLP blocks (gate/up/down) are NVFP4. Embeddings, `lm_head` and small projections stay BF16 per the recipe. Calibration ran on **512 samples × 512 tokens** (262144 tokens) of competitive-programming code from `nvidia/Nemotron-Competitive-Programming-v1`, keeping quantization faithful on the distributions that matter most for coding tasks. The result retains the original model's strong software-engineering capabilities at roughly half of the BF16 footprint, ready for NVFP4-capable inference stacks on Blackwell GPUs. ## Files | File | Size | Notes | |---|---|---| | `model-00001-of-00008.safetensors` … `model-00008-of-00008.safetensors` | ~29.2 GB total | NVFP4 MLP weights (packed U8 + FP8 block scales) + BF16 attention/GDN/embeddings/head | | `model.safetensors.index.json` | — | Shard index (1427 tensors) | | `hf_quant_config.json` | — | `quant_algo: NVFP4`, group size 16 | | `config.json`, `tokenizer.*`, `merges.txt`, `vocab.json` | — | Config + tokenizer from the base model | | `scripts/quantize_veriloop.py` | — | Full reproduction script (multi-GPU PTQ) | | `scripts/validate_nvfp4.py` | — | Structural shard/index validator | ## Usage This checkpoint targets **NVFP4-capable runtimes** (Blackwell GPUs). Plain `transformers` cannot load modelopt NVFP4 checkpoints directly. > **Compatibility note:** per the NVIDIA recipe, the `linear_attn` blocks, the self-attention projections, a small set of projections (`linear_attn.in_proj_a/b`, `conv1d`), plus the embeddings and `lm_head`, intentionally ship in BF16. The `exclude_modules` list in `hf_quant_config.json` (mirrored in `config.json`, including fused names such as `in_proj_ba`/`in_proj_qkvz` that some loaders synthesize) declares exactly which modules those are — inference loaders must honor it and serve those modules unquantized. If a loader errors with *"weight is torch.bfloat16 but the quant config declares NVFP4"*, it is ignoring the exclusion list, not a checkpoint defect. ### vLLM ```bash pip install 'vllm>=0.17.0' vllm serve rodrigoramosrs/veriloop-coder-e2-nvfp4 \ --tensor-parallel-size 1 \ --max-model-len 131072 \ --kv-cache-dtype fp8_e4m3 \ --gpu-memory-utilization 0.92 ``` ### SGLang ```bash pip install 'sglang[all]' python -m sglang.launch_server \ --model-path rodrigoramosrs/veriloop-coder-e2-nvfp4 \ --trust-remote-code \ --mem-fraction-static 0.88 ``` ### TensorRT-LLM Build the engine from this checkpoint with `trtllm-build` using an NVFP4 + FP8-KV configuration, then serve with `trtllm-serve`. See the [TensorRT-LLM documentation](https://github.com/NVIDIA/TensorRT-LLM) for the exact flags for your GPU. ### Reproducing ```bash pip install "nvidia-modelopt[all]" torch transformers accelerate datasets safetensors # needs ~55 GB VRAM across CUDA GPUs for the 27B BF16 source model python scripts/quantize_veriloop.py \ --model ./model-bf16 \ --output ./model-nvfp4 \ --calib-size 512 \ --calib-seq-len 512 python scripts/validate_nvfp4.py ./model-nvfp4 ``` On Windows the Triton JIT needs a C compiler: run from a Visual Studio Native Tools prompt (`vcvars64`) with `CC` pointing at `cl.exe`. See the header of `scripts/quantize_veriloop.py` for multi-GPU layout tuning (`--layers-split`, `--gpu-order`). ## Runtime notes - **Verified working:** FreeToken (NVFP4, Blackwell) and SGLang ≥ 0.5.20 with the loader patch below — end-to-end generation confirmed on both. - **SGLang ≤ 0.5.20, stock:** serving this checkpoint yields degenerate output (constant-token repetition). Root cause, bisected: `qwen3_5_text.Qwen3_5ForCausalLM.load_weights` strips the leading `model.` before delegating to a body whose params are prefix-less, so essentially every weight misses (`... not found in params_dict` warnings) and the model runs uninitialized. Fix (`scripts/sglang-qwen35-loader.patch.py`, applied in `scripts/Dockerfile.sglang-qwen35fix`): strip `model.` plus an optional `language_model.` segment, yielding exactly the body-relative names for both `model.layers.*` and `model.language_model.*` checkpoints. Affects any text-only Qwen3.5 checkpoint on stock 0.5.20, quantized or not. ## Acknowledgements - **Libo Wang** and the **Intelligent Robotics Laboratory, Tsinghua SIGS** for developing the original VeriLoop E2 model. - **NVIDIA** for Model Optimizer and the NVFP4 quantization tooling. - The original model repository: [tsinghua-sigs-robot-lab/VeriLoop-E2](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2) ## License Apache-2.0. The weights are quantized from the original Apache-2.0 licensed model. See the [original repository](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2) for full licensing details and third-party notices.