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--- |
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license: apache-2.0 |
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: TinyLlama_instruct_generation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# TinyLlama_instruct_generation |
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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. |
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## Model description |
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This model has been fine tuned with mosaicml/instruct-v3 dataset with 2 epoch only. Mainly this model is useful for RAG based application |
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## How to use? |
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from peft import PeftModel |
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#load the base model |
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model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" |
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tokenizer=AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype = torch.bfloat16, |
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device_map = "auto", |
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trust_remote_code = True |
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) |
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#load the adapter |
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model_peft = PeftModel.from_pretrained(model, "azam25/TinyLlama_instruct_generation") |
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messages = [{ |
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"role": "user", |
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"content": "Act as a gourmet chef. I have a friend coming over who is a vegetarian. \ |
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I want to impress my friend with a special vegetarian dish. \ |
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What do you recommend? \ |
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Give me two options, along with the whole recipe for each" |
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}] |
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def generate_response(message, model): |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False) |
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encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True) |
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model_inputs = encoded_input.to('cuda') |
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generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id) |
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decoded_output = tokenizer.batch_decode(generated_ids) |
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return decoded_output[0] |
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response = generate_response(messages, model) |
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print(response) |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_steps: 0.03 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.6386 | 1.0 | 25 | 1.4451 | |
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| 1.5234 | 2.0 | 50 | 1.3735 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
