File size: 2,762 Bytes
a8c7554 a9b55df a8c7554 a9b55df a8c7554 0d74320 94cf888 0d74320 94cf888 0d74320 94cf888 0d74320 94cf888 0d74320 94cf888 0d74320 94cf888 0d74320 a8c7554 a9b55df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
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 |