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README.md

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+---
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- llama-factory
+- lora
+- transformers
+model-index:
+- name: llama31-8b-hatexplain-lora (checkpoint-3500)
+  results:
+  - task:
+      type: text-classification
+      name: Hate speech classification
+    dataset:
+      name: HateXplain
+      type: hatexplain
+      split: validation
+    metrics:
+    - type: accuracy
+      value: 0.7196
+    - type: f1
+      name: macro_f1
+      value: 0.6998
+---
+
+# Llama 3.1 8B Instruct — HateXplain (LoRA adapter, ckpt-3500)
+
+- Base model: `meta-llama/Meta-Llama-3.1-8B-Instruct`
+- Adapter: LoRA (rank=8, alpha=16, dropout=0.05, target=all)
+- Trainable params: ~0.26% (≈20.97M / 8.05B)
+- Task: hate speech detection (3 classes: `hatespeech`, `offensive`, `normal`)
+- Dataset: HateXplain (train/validation)
+- Epochs: 3
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+adapter = "<your-username>/<your-adapter-repo>"  # or local path to this checkpoint
+
+tokenizer = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter)
+
+# Build a llama3-style prompt and generate the label ("hatespeech"/"offensive"/"normal")
+```
+
+## Training Configuration
+
+- Finetuning type: LoRA
+- LoRA: rank=8, alpha=16, dropout=0.05, target=all
+- Precision: bf16 (with gradient checkpointing)
+- Per-device batch size: 1
+- Gradient accumulation: 8 (effective batch = 8)
+- Learning rate: 5e-5, scheduler: cosine, warmup ratio: 0.05
+- Epochs: 3
+- Template: `llama3`, cutoff length: 2048
+- Output dir: `saves/llama31-8b/hatexplain/lora`
+
+## Data
+
+- Source: HateXplain (`datasets`), majority-vote label from annotators
+- Splits: train (15,383), validation (1,922)
+- Format: Alpaca-style with `instruction` + `input` → label as assistant response
+
+## Evaluation (validation)
+
+Overall:
+- Accuracy: 0.7196
+- Macro-F1: 0.6998
+
+Per-class report:
+
+```
+              precision    recall  f1-score   support
+
+  hatespeech     0.7252    0.8634    0.7883       593
+   offensive     0.6152    0.4726    0.5346       548
+      normal     0.7698    0.7836    0.7766       781
+
+    accuracy                         0.7196      1922
+   macro avg     0.7034    0.7065    0.6998      1922
+weighted avg     0.7120    0.7196    0.7112      1922
+```
+
+Notes:
+- Decoding is greedy with short label strings; loss-based and log-likelihood scoring produce similar ordering.
+- Ensure tokenizer uses `pad_token = eos_token` for batched evaluation.
+
+## Limitations and Intended Use
+
+- For moderated content classification; not intended for generating harmful content.
+- Biases present in the dataset may be reflected in predictions.
+
+## Acknowledgements
+
+- Built with [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) and PEFT.
+