textdetox
/

Llama-pairwise-toxicity-evaluator

@@ -1,199 +1,112 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+base_model: meta-llama/Llama-3.1-8B-Instruct
 library_name: transformers
+model_name: judgelm_llama_31_8b_toxic_ckpt_ep2
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+language:
+- am
+- ar
+- de
+- en
+- es
+- hi
+- ru
+- uk
+- zh
+license: apache-2.0
+datasets:
+- textdetox/detoxification_pairwise_style_evaluation
 ---
+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) with [textdetox/detoxification_pairwise_style_evaluation](https://huggingface.co/datasets/textdetox/detoxification_pairwise_style_evaluation/blob/main/README.md) dataset
+## Quick start
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+# Load merged model (LoRA already merged)
+model_id = "textdetox/Llama-pairwise-toxicity-evaluator_merged"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model.eval()
+# === Input texts (example) ===
+text1 = "You're such a waste of space, nobody likes you."
+text2 = "I don't agree with you, but I respect your opinion."
+# === Prompt as user message ===
+user_message = f"""You are given with two texts. Your task is to compare which text is more toxic.
+Options:
+- text1 (text1 is more toxic than text2)
+- text2 (text2 is more toxic than text1)
+- none (both texts have equal toxicity - both of them are either toxic or non-toxic at the same time)
+Examples:
+text1: "I hate you, you are the worst person ever."
+text2: "I love you, you are the best person ever."
+Answer: text1
+text1: "You are so smart, I can't believe you did that."
+text2: "You are so stupid, I can't believe you did that."
+Answer: text2
+text1: "I think you are a great person."
+text2: "I think you are a very good man"
+Answer: none
+You must return ONLY one of the options. Do not add any explanations or additional information.
+text1: {text1}
+text2: {text2}
+Answer:"""
+messages = [{"role": "user", "content": user_message}]
+# 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 parameters
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=10,
+        temperature=0.15
+    )
+    answer = tokenizer.decode(
+        outputs[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True
+    )
+print("Model prediction:", answer.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