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--- |
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license: apache-2.0 |
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library_name: peft |
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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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base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.3 |
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model-index: |
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- name: tinyllama-colorist-lora-v0.3 |
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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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<h1 align="center"><font color="red">tinyllama-colorist-lora-v0.3</font></h1> |
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This model, `tinyllama-colorist-lora-v0.3`, is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) on the color dataset. |
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## <font color="yellow">Study Motivation</font> |
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To study this new TinyLlama model as a replacement for Llama2 for resource-constrained environment. Also, in the future I will perform the Fine-Tuning of this model for Chat and for a specific domain in Portuguese and Spanish 🤗. |
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## <font color="yellow">Prompt format</font> |
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The model training process is similar to the regular Llama2 model with a chat prompt format like this: |
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``` |
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<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n{answer}<|im_end|>\n |
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``` |
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## <font color="yellow">Instructions for use</font> |
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``` |
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User Input: Give me a sky blue color. |
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LLM response: #6092ff |
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``` |
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## <font color="yellow">Model usage</font> |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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from transformers import pipeline |
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def print_color_space(hex_color): |
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def hex_to_rgb(hex_color): |
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hex_color = hex_color.lstrip('#') |
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return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) |
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r, g, b = hex_to_rgb(hex_color) |
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print(f'{hex_color}: \033[48;2;{r};{g};{b}m \033[0m') |
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tokenizer = AutoTokenizer.from_pretrained(model_id_colorist_final) |
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pipe = pipeline( |
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"text-generation", |
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model=model_id_colorist_final, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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from time import perf_counter |
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start_time = perf_counter() |
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prompt = formatted_prompt('give me a pure brown color') |
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sequences = pipe( |
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prompt, |
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do_sample=True, |
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temperature=0.1, |
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top_p=0.9, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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max_new_tokens=12 |
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) |
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for seq in sequences: |
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print(f"Result: {seq['generated_text']}") |
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output_time = perf_counter() - start_time |
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print(f"Time taken for inference: {round(output_time,2)} seconds") |
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``` |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 200 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
