import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from unsloth import is_bfloat16_supported

# Precision
dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16

# Models
BASE_MODEL = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
LORA_MODEL = "sag-uniroma2/FrameLLaMA-3.1-8B-Instruct-FullFN17"

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=dtype,
    device_map="auto"
)

# Load LoRA
model = PeftModel.from_pretrained(base_model, LORA_MODEL)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(LORA_MODEL, use_fast=True)

# Device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# 🔥 ===== YOUR SAMPLE HERE =====
premise = "John drowned Martha."
hypothesis = "Martha died."

# Prompt
input_text = f"""
        Judge if the hypothesis necessarily follows from the premise.
        Consider the truth value of the premise. If the premise is true, does it necessarily mean that the hypothesis must also be true?
        Output E if the hypothesis must always be true.
        Output C if the hypothesis must always be false.
        Output N if the hypothesis may be either true or false.
        Do not output anything other than letters E, C, or N.

        Premise: {premise}
        Hypothesis: {hypothesis}
        # Output:"""

# Tokenize
inputs = tokenizer(input_text, return_tensors="pt").to(device)

# Generate
with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=10,
        do_sample=False
    )

# Decode only generated part
input_len = inputs["input_ids"].shape[1]
generated = output_ids[0][input_len:]
response = tokenizer.decode(generated, skip_special_tokens=True).strip()

# Print result
print("Premise:", premise)
print("Hypothesis:", hypothesis)
print("Prediction:", response)

Description

FrameLLaMA-3.1-8B-Instruct-FullFN17 is a frame-aware language model designed to improve event-level semantic reasoning in Large Language Models (LLMs). The model injects structured knowledge from FrameNet 1.7 into Llama-3.1-8B-Instruct using parameter-efficient LoRA fine-tuning.

Unlike standard instruction tuning, this model leverages principle-oriented supervision, where frame definitions, participant roles, semantic types, lexical senses, and frame-to-frame relations are converted into structured question–answer tasks. This enables the model to learn reusable semantic constraints rather than isolated facts.

The model is optimized for tasks where meaning depends on event structure, participant roles, and lexical disambiguation, such as Natural Language Inference (NLI) and Semantic Role Labeling (SRL).

Model Details

Base Model: Llama-3.1-8B-Instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Data: FrameNet 1.7 (full inventory, 1,200+ frames)
Supervision Type: Principle-oriented QA-style prompts
Tasks: NLI, SRL (evaluation), semantic reasoning
Model Type: Instruction-tuned causal language model

Key Features

✅ Full FrameNet Coverage: Trained on 1,200+ frames.
✅ Principle-Oriented Learning: Encodes role constraints, semantic types, and frame relations
✅ Event-Level Reasoning: Improves understanding of causality, entailment, and contradiction
✅ Frame-Aware Inference: Better handling of lexical ambiguity and role compatibility
✅ Parameter-Efficient Training: Uses LoRA for scalable adaptation
✅ Generalization Beyond SRL: Transfers to NLI and semantic inference tasks

Performance

Evaluated on:
- SNLI (diagnostic subset) for event-level inference
- CONLL-style FrameNet SRL dataset (via OpenSesame preprocessing)
Observed improvements:
- Strong gains in entailment and contradiction detection
- Improved frame identification and role-span alignment in SRL
- Reduced reliance on surface-level lexical cues

Use Cases

Natural Language Inference (NLI): Event-based reasoning and entailment detection
Semantic Role Labeling (SRL): Frame and role prediction
Event Understanding: Modeling causality and participant structure
Linguistically-Informed AI: Applications requiring structured semantic interpretation
Research on LLM Interpretability: Studying structured knowledge injection

Output Format

Single token or short response:
- E → Entailment
- C → Contradiction
- N → Neutral
Outputs are concise and reflect event-level semantic reasoning.

Training Details

FrameNet structures (definitions, roles, relations) are linearized into QA-style templates
Supervision includes:
- Frame definitions
- Role constraints
- Semantic types
- Lexical unit disambiguation
- Frame-to-frame relations
Negative samples generated via similarity-based filtering
Fine-tuned using LoRA for efficiency and scalability

sag-uniroma2/
FrameLLaMA-3.1-8B-Instruct-FullFN17

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