TinyLlama_instruct_generation

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the generator dataset.

Model description

This model has been fine tuned with mosaicml/instruct-v3 dataset with 2 epoch only. Mainly this model is useful for RAG based application

How to use?

from peft import PeftModel

#load the base model

model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

tokenizer=AutoTokenizer.from_pretrained(model_path)

model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype = torch.bfloat16, device_map = "auto", trust_remote_code = True )

#load the adapter

model_peft = PeftModel.from_pretrained(model, "azam25/TinyLlama_instruct_generation")

messages = [{ "role": "user", "content": "Act as a gourmet chef. I have a friend coming over who is a vegetarian.
I want to impress my friend with a special vegetarian dish.
What do you recommend?
Give me two options, along with the whole recipe for each" }]

def generate_response(message, model):

prompt = tokenizer.apply_chat_template(messages, tokenize=False) encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True) model_inputs = encoded_input.to('cuda') generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id) decoded_output = tokenizer.batch_decode(generated_ids) return decoded_output[0]

response = generate_response(messages, model) print(response)

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: 0.0002
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_steps: 0.03
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.6386	1.0	25	1.4451
1.5234	2.0	50	1.3735

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0