---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: judgelm_llama_31_8b_content_ckpt_ep2
tags:
- generated_from_trainer
- trl
- sft
licence: license
language:
  - am
  - ar
  - de
  - en
  - es
  - hi
  - ru
  - uk
  - zh
datasets:
  - textdetox/detoxification_pairwise_style_evaluation
---

# Model Card for 

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

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

# Load merged model (LoRA already merged)
model_id = "textdetox/Llama-pairwise-content-evaluator_merged"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

model.eval()

# Prompt with example + test pair
messages = [
    {
        "role": "user",
        "content": '''You are given with two texts. Your task is to compare the meaning of them and decide whether they mean the same or not.

Options:
- yes (both texts have the same meaning)
- no (there is significant difference in meaning between the two texts)

Examples:
text1: "Why do you behave like an asshole?"
text2: "Why do you act so bad?"
Answer: yes

text1: "This political party is a gang of idiots"
text2: "They just don’t know their stuff"
Answer: no

text1: "There is nothing dangerous here. People like Abraham Desta who try to create chaos are just opponents of the old system."
text2: "Nothing dangerous is happening. People like Abraham Desta who try to stir things up are just enemies of the previous regime."
Answer:'''
    }
]

# Apply chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

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

# Generate
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=5, temperature=0.15)
    result = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    )

print("Model prediction:", result.strip())


```


### Training framework versions

- TRL: 0.16.0
- Transformers: 4.50.1
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1

## Citations