Semaj90/gemma4-e4b-legal-grpo

Gemma 4 E4B Legal GRPO — LoRA Adapter

Fine-tuned Google Gemma 4 E4B for legal analysis using GRPO (Group Relative Policy Optimization) reinforcement learning.

Model Details

Model Description

Legal-domain LoRA adapter trained with GRPO reinforcement learning on 45K+ legal/reasoning examples. Optimized for legal document analysis, statute interpretation, evidence classification, and case law reasoning with proper Bluebook citations.

• —Developed by: Semaj90
• —Model type: PEFT LoRA adapter (text generation)
• —Language: English
• —License: Apache 2.0
• —Fine-tuned from: google/gemma-4-E4B-it

Model Sources

• —Repository: Semaj90/gemma4-e4b-legal-grpo
• —GRPO Paper: DeepSeekMath (arXiv:2402.03300)

How to Get Started

With Unsloth (recommended)

from unsloth import FastVisionModel
from peft import PeftModel

model, tokenizer = FastVisionModel.from_pretrained(
    model_name="unsloth/gemma-4-E4B-it-unsloth-bnb-4bit",
    max_seq_length=4096,
    load_in_4bit=True,
)
model = PeftModel.from_pretrained(model, "Semaj90/gemma4-e4b-legal-grpo")
FastVisionModel.for_inference(model)

messages = [{"role": "user", "content": [{"type": "text", "text": "Analyze 42 U.S.C. Section 1983"}]}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
output = model.generate(input_ids=inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))

With Ollama (GGUF)

# See: Semaj90/gemma4-e4b-legal-grpo-GGUF
ollama create gemma4-legal:latest -f Modelfile
ollama run gemma4-legal:latest "What are the elements of negligence?"

Uses

Direct Use

• —Legal document analysis and case law research
• —Evidence classification and chain of custody assessment
• —Statute interpretation (U.S.C., CFR, state codes)
• —Case law reasoning and precedent analysis
• —Contract review and liability assessment
• —Legal AI chatbot / RAG pipeline augmentation

Out-of-Scope Use

• —Not a substitute for professional legal advice
• —Not designed for jurisdictions outside U.S. law
• —Should not be used for automated legal decision-making without human review

Training Details

Training Data

45K+ examples distilled into 1,000 GRPO prompts (Phase 1 pilot).

Training Procedure

Method: GRPO (Group Relative Policy Optimization) — generates multiple completions per prompt, scores them with reward functions, and updates the policy to favor higher-reward outputs.

Training Hyperparameters

• —Training regime: BFloat16
• —Optimizer: AdamW 8-bit
• —Learning rate: 5e-6 (cosine schedule)
• —Batch size: 4 (gradient accumulation: 2, effective: 8)
• —Generations per prompt: 2
• —Max completion length: 256 tokens
• —Warmup steps: 30
• —Weight decay: 0.01
• —Max grad norm: 0.5
• —Epochs: 1

Reward Functions

Single combined reward with 5 signals optimized for training throughput:

Speeds, Sizes, Times

• —Total training time: ~1.3 hours (4,744 seconds)
• —Steps: 250
• —Throughput: 0.21 samples/sec
• —Hardware: NVIDIA RTX PRO 6000 Blackwell Edition (95 GB VRAM)
• —Cloud: Google Colab G4 GPU

Bias, Risks, and Limitations

• —May hallucinate case citations despite anti-hallucination reward signal
• —Trained primarily on U.S. federal law — limited state and international coverage
• —Text-only adapter — vision/audio capabilities of base Gemma 4 pass through unmodified
• —Phase 1 pilot training (1K prompts, 1 epoch) — production training with full dataset recommended

Adapter Surgery Note

The original training produced 884 tensors (588 language + 224 vision + 72 audio). Despite finetune_vision_layers=False, generic target_modules (q_proj, k_proj, etc.) matched projections across all sub-models. The vision/audio tensors use Gemma4ClippableLinear which PEFT cannot merge (upstream issue).

This adapter has been surgically cleaned to contain only the 588 language_model tensors (140 MB). Vision and audio capabilities pass through from the base model unmodified.

Environmental Impact

• —Hardware Type: NVIDIA RTX PRO 6000 Blackwell
• —Hours used: ~1.3
• —Cloud Provider: Google Colab
• —Compute Region: US

Technical Specifications

Compute Infrastructure

• —Hardware: NVIDIA RTX PRO 6000 Blackwell Edition (95 GB, Compute 12.0)
• —Software: Unsloth 2026.4.2, TRL 0.24.0, PEFT 0.18.1, Transformers 5.5.0, PyTorch 2.10.0+cu128

Citation

@misc{gemma4-legal-grpo-2026,
  title={Gemma 4 E4B Legal GRPO Adapter},
  author={Semaj90},
  year={2026},
  url={https://huggingface.co/Semaj90/gemma4-e4b-legal-grpo}
}

Framework Versions

• —PEFT 0.18.1
• —Unsloth 2026.4.2
• —TRL 0.24.0
• —Transformers 5.5.0
• —PyTorch 2.10.0+cu128