Entropy v1 FP8 is a merged + FP8-quantized checkpoint based on google/gemma-3-27b-it, fine-tuned to rewrite AI-polished text into more human-sounding prose while preserving meaning.
This repo is intended for efficient inference in vLLM without runtime LoRA.
What It Does
Given an AI-sounding passage, the model rewrites it to be:
More human and textured (less generic "professional polish")
More varied in rhythm/word choice
Meaning-preserving (style change, not content change)
Prompt Trigger (Recommended)
This is the pattern used in our fine-tuning data. Keep the passage after a newline.
text
1Polish this AI passage to feel more human:
2{passage}
Short variants that usually work similarly:
Rephrase this AI passage to feel more human:\n{passage}
Convert this AI passage into a more human-sounding version:\n{passage}
This checkpoint is already quantized (compressed-tensors FP8_DYNAMIC). You do not need to pass --quantization fp8.
FP8 execution is hardware-dependent; see "Quantization" below.
2) Send a request
bash
1curl http://127.0.0.1:8000/v1/chat/completions \2 -H 'Content-Type: application/json'\3 -H 'Authorization: Bearer sk-noop'\4 -d '{
5 "model": "entropy-v1-fp8",
6 "messages": [
7 {
8 "role": "user",
9 "content": "Polish this AI passage to feel more human:\nThis is a highly polished paragraph that sounds generic and overly smooth..."
10 }
11 ],
12 "temperature": 0.7,
13 "top_p": 0.95,
14 "max_tokens": 512
15 }'
Validation Benchmark (70 Gutenberg Examples)
We evaluate by computing the conditional negative log-likelihood of the target (human) rewrite given the prompt, and report character-normalized bits_per_char:
Let NLL be the sum of token NLL over the target rewrite (teacher-forced).
Let C be the number of characters in the target rewrite.
bits_per_char = (NLL / C) / ln(2).
This char-normalization makes the score more comparable across models/tokenizers than token-based perplexity.
Lower is better.
Results
Baseline for relative improvement: N8Programs/Unslopper-30B-A3B-bf16.
System
bits_per_char (↓)
Relative improvement vs Unslopper (↑)
Entropy v1 FP8 (this repo)
0.35994
+4.07%
N8Programs/Unslopper-30B-A3B-bf16
0.37522
+0.00%
Base google/gemma-3-27b-it
0.99565
-165.35%
Interpretation:
Entropy v1 FP8 achieves the best bits_per_char on this 70-example Gutenberg validation study.
Quantization (Merged FP8_DYNAMIC)
This checkpoint is produced in two steps:
Merge: a PEFT LoRA adapter is merged into the base Gemma 3 27B IT weights (no runtime LoRA).
Quantize: we apply FP8_DYNAMIC (W8A8) quantization with llm-compressor:
Targets: all Linear layers in the language model
Weights: FP8, static per-channel scaling
Activations: FP8, dynamic per-token scaling
Ignored: lm_head and the Gemma 3 vision tower (left in BF16)
The model is saved in a vLLM-loadable compressed-tensors format.
Hardware notes (vLLM):
Hopper/Ada/Blackwell-class NVIDIA GPUs can execute FP8 efficiently.
Other GPUs may fall back to less optimized modes.
Throughput (vLLM)
Measured on a single NVIDIA RTX PRO 6000 Blackwell 96GB using vllm/vllm-openai:v0.11.2 with random prompts:
Input length: 512 tokens
Output length: 256 tokens
max_concurrency
output tok/s
total tok/s
1
25.87
77.51
20
412.60
1236.20
Limitations / Misuse
Trained primarily on literary/public-domain style passages; performance may vary on technical/legal writing.
Like other "humanizer" models, it can be misused for deceptive purposes. Use responsibly and follow applicable policies and disclosure norms.
