---
license: llama3.1
language: en
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---

# Self-Corrective Llama 3.1 8B

This is a fine-tuned version of `meta-llama/Meta-Llama-3.1-8B-Instruct`, augmented with a novel self-correction mechanism designed to mitigate hallucinations. The LoRA adapter has been merged into the base model for easy deployment.

This model features a custom **hallucination detection head** that works in parallel with the main language model. When it detects a potential error in its own generated text, it can insert special instructions like `[rewrite sentence]` or `[rewrite response]` into the output, effectively flagging its own mistakes for correction. This makes the model more reliable for tasks requiring factual accuracy.

## How it Works

The model, an instance of the custom `SelfCorrectiveLlama` class, adds a small, efficient hallucination detection module to the standard Llama architecture. This module analyzes the model's internal states (hidden states) at each generation step to predict the likelihood of a hallucination.

The model's custom `generate` method then uses these predictions. If a hallucination is likely, it overrides the standard token generation process to insert a corrective instruction. This entire process happens in a single forward pass, making it significantly more efficient than multi-step, agent-based correction pipelines that require multiple LLM calls.

## Intended Use & Prompting

This model is intended for tasks where factual accuracy and faithfulness to a source context are critical, such as question answering or summarization.

While it can be used with standard prompts, its self-correction behavior was reinforced during training using a specific instruction. To achieve the best results and fully leverage the self-correction mechanism, you should include the following note in your system prompt or at the beginning of your input:

<br>

> **Note on Self-Correction**: As you generate your response, you may encounter an automated instruction. This indicates a potential error was detected.
> - If you see the instruction `[rewrite sentence]`, it means the preceding sentence is incorrect. You must immediately provide a new, corrected version of that sentence.
> - If you see the instruction `[rewrite response]`, it means the entire preceding response is incorrect. You must immediately provide a new, complete response from the beginning.

<br>

## How to Use

Because this model uses a custom architecture with a modified `generate` method, you **must** use `trust_remote_code=True` when loading it. The required `modeling.py` file is included in this repository.

**Note:** The custom `generate` method currently only supports a **batch size of 1**.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "MathBite/self_corrective_llama_3.1_8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Important: You must trust the remote code
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16 # or your preferred dtype
).to("cuda") # move model to GPU

# Example prompt with the self-correction instruction
prompt = """
...
Note on Self-Correction: As you generate your response, you may encounter an automated instruction. This indicates a potential error was detected.
- If you see the instruction `[rewrite sentence]`, it means the preceding sentence is incorrect. You must immediately provide a new, corrected version of that sentence.
- If you see the instruction `[rewrite response]`, it means the entire preceding response is incorrect. You must immediately provide a new, complete response from the beginning.

---

Context: The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower.

Question: Who was the first person to climb the Eiffel Tower?
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

# The custom generate method requires the tokenizer instance
generated_ids = model.generate(
    inputs.input_ids,
    tokenizer=tokenizer,
    max_new_tokens=100,
    temperature=0.7
)

generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(generated_text)
```

## Model Details

This model was programmatically merged and uploaded using a deployment script. The custom class `SelfCorrectiveLlama` can be found in the `modeling.py` file included in this repository.

The code in `modeling.py` is licensed under the Apache 2.0 License. The model weights are subject to the original license of the base model.