---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: TinyLlama_instruct_generation
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# TinyLlama_instruct_generation

This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/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