PhigRange-DPO
PhigRange-DPO is a DPO fine-tuned of johnsnowlabs/PhigRange-2.7B-Slerp using the mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha preference dataset. The model has been trained for for 1080 steps.
π Evaluation results
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π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/PhigRange-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 1
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: AdamOptimizer32bit
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1080
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0
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