---
pipeline_tag: text-generation
license: apache-2.0
base_model:
  - tsinghua-sigs-robot-lab/VeriLoop-E2
language:
  - en
  - zh
tags:
  - gguf
  - llama.cpp
  - mtp
  - speculative-decoding
  - dflash2
  - veriloop
  - apache-2.0
---

This is an experimental research artifact, it does actually run but I can't verify acceptance rates because my 12gb of vram apparently doesnt cut it because kv caches and all that

But yeah it does work- this was trained on a laptop gpu which is kinda crazy ngl

if you want to try it- use [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)

heres the self eval:

``` bash
python -m toffee xspark eval --head xspark_out/dflash2/dflash2_head.pt \
          --target-model ./VeriLoop-E2 --data-dir xspark_data/harvest --anchors 512
{"name": "NVIDIA GeForce RTX 4080 Laptop GPU", "vram_gb": 11.67608642578125, "event": "gpu_detected", "level": "info", "timestamp": "2026-11-06T14:22:03.355667Z"}
{
  "variant": "dflash2",
  "n_blocks": 512,
  "block_size": 8,
  "accept_len": 1.171875,
  "oracle_len": 2.068359375,
  "accept@0": 0.15625,
  "recall@0": 0.49609375,
  "accept@1": 0.015625,
  "recall@1": 0.26171875,
  "accept@2": 0.0,
  "recall@2": 0.13671875,
  "accept@3": 0.0,
  "recall@3": 0.078125,
  "accept@4": 0.0,
  "recall@4": 0.048828125,
  "accept@5": 0.0,
  "recall@5": 0.029296875,
  "accept@6": 0.0,
  "recall@6": 0.017578125
} 
```

What the numbas say:

| metric | value | plain english |
|---|---|---|
| `recall@0` | 254/512 = **49.6%** | the true next token is *in your top-16* half the time — the head **knows** it |
| `accept@0` | 80/512 = **15.6%** | the walk *picks* it 16% of the time |
| gap | **174 anchors** | truth was sitting in the candidate list and the selector walked past it |
| `oracle_len` | **2.068** | accept length if the walk always picked the truth when available — the ceiling **of this head as-is** |
| `accept_len` | **1.172** | you're capturing (1.17−1)/(2.07−1) ≈ **16% of the available headroom** |


if you want to recreate this abomination use my [llm toolkit](https://github.com/damien1141/toffee)