Update README.md

README.md

@@ -1,3 +1,135 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+base_model:
+- AIR-hl/Qwen2.5-1.5B-ultrachat200k
+pipeline_tag: text-generation
+tags:
+- trl
+- qwen
+- dpo
+- alignment
+- transformers
+- custome
+- chat
+---
+# Qwen2.5-1.5B-WPO
+
+
+## Model Details
+
+- **Model type:** aligned model
+- **License:** Apache license 2.0
+- **Finetuned from model:** [AIR-hl/Qwen2.5-1.5B-ultrachat200k](https://huggingface.co/AIR-hl/Qwen2.5-1.5B-ultrachat200k)
+- **Training data:** [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
+- **Training framework:** [trl](https://github.com/huggingface/trl)
+
+## Training Details
+
+devices: 4 * NPU 910B-64GB \
+precision: bf16 mixed-precision \
+global_batch_size: 128
+
+### Training Hyperparameters
+`attn_implementation`: None \
+`beta`: 0.01 \
+`bf16`: True \
+`learning_rate`: 1e-6 \
+`lr_scheduler_type`: cosine \
+`per_device_train_batch_size`: 8 \
+`gradient_accumulation_steps`: 4 \
+`torch_dtype`: bfloat16 \
+`num_train_epochs`: 1 \
+`max_prompt_length`: 512 \
+`max_length`: 1024 \
+`warmup_ratio`: 0.05
+
+### Results
+
+`init_train_loss`: 0.2410 \
+`final_train_loss`: 0.1367 \
+`accuracy`: 0.65 \
+`reward_margin`: 0.2402
+
+### Training script
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import multiprocessing
+from trl import (
+    DPOConfig,
+    DPOTrainer,
+    ModelConfig,
+    ScriptArguments,
+    TrlParser,
+    get_kbit_device_map,
+    get_peft_config,
+    get_quantization_config,
+)
+from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
+
+if __name__ == "__main__":
+    parser = TrlParser((ScriptArguments, DPOConfig, ModelConfig))
+    script_args, training_args, model_config = parser.parse_args_and_config()
+
+    torch_dtype = (
+        model_config.torch_dtype
+        if model_config.torch_dtype in ["auto", None]
+        else getattr(torch, model_config.torch_dtype)
+    )
+
+    quantization_config = get_quantization_config(model_config)
+
+    model_kwargs = dict(
+        revision=model_config.model_revision,
+        attn_implementation=model_config.attn_implementation,
+        torch_dtype=torch_dtype,
+        use_cache=False if training_args.gradient_checkpointing else True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, **model_kwargs
+    )
+
+    peft_config = get_peft_config(model_config)
+    if peft_config is None:
+        ref_model = AutoModelForCausalLM.from_pretrained(
+            model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, **model_kwargs
+        )
+    else:
+        ref_model = None
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code
+    )
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    if tokenizer.chat_template is None:
+        tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
+    if script_args.ignore_bias_buffers:
+        model._ddp_params_and_buffers_to_ignore = [
+            name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
+        ]
+
+    dataset = load_dataset(script_args.dataset_name,
+                           split=script_args.dataset_train_split)
+    dataset=dataset.select_columns(['chosen', 'prompt', 'rejected'])
+
+    trainer = DPOTrainer(
+        model,
+        ref_model,
+        args=training_args,
+        train_dataset=dataset,
+        processing_class=tokenizer,
+        peft_config=peft_config,
+    )
+
+    trainer.train()
+
+    trainer.save_model(training_args.output_dir)
+```