metadata
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
datasets:
- teknium/OpenHermes-2.5
model-index:
- name: phi-2-OpenHermes-2.5
results: []
phi-2-OpenHermes-2.5
This model is a fine-tuned version of microsoft/phi-2 on the teknium/OpenHermes-2.5 dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
Inference
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "minghaowu/phi-2-OpenHermes-2.5"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
your_instruction = <your_instruction>
infer_prompt = f"### USER: {your_instruction} <|endoftext|>\n### ASSISTANT:"
output = pipe(infer_prompt, do_sample=True, max_new_tokens=256)[0]["generated_text"]
print(output)