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--- |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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language: |
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- en |
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base_model: |
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- unsloth/Llama-3.2-3B |
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pipeline_tag: text-generation |
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license: mit |
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--- |
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### Model Description |
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This model is a fine-tuned version of **`unsloth/Meta-Llama-3.2-3B`** optimized for **Prompt Generation** tasks when given a act. The fine-tuning was done using the **Unsloth library** with LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning. The training was done on **fka/awesome-chatgpt-prompts** dataset. |
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- **Developed by**: Vedant Rajpurohit |
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- **Model type**: Causal Language Model |
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- **Language(s)**: English |
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- **Fine-tuned from model**: `unsloth/Meta-Llama-3.2-3B` |
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- **Precision**: F32 |
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### Direct Use |
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```python |
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# !pip install bitsandbytes peft |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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# Load the tokenizer for the base model |
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tokenizer = AutoTokenizer.from_pretrained("Vedant3907/Prompt-Generator-Lora-model", use_fast=False) |
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# Load the base model in 4-bit quantization mode |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"Vedant3907/Prompt-Generator-Lora-model", |
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# load_in_4bit=True, |
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trust_remote_code=True |
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) |
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gpt_prompt = """ |
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### Instruction: |
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{} |
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### Response: |
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{}""" |
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inputs = tokenizer( |
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[ |
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gpt_prompt.format( |
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"Rapper", # instruction |
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"", # output - leave this blank for generation! |
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) |
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], return_tensors = "pt").to("cuda") |
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outputs = base_model.generate(**inputs, max_new_tokens = 200, use_cache = True) |
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tokenizer.batch_decode(outputs) |
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""" |
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'<|begin_of_text|> |
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### Instruction: |
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Rapper |
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### Response: |
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I want you to act as a rapper. You will come up with powerful and meaningful lyrics, beats and rhythm that can ‘wow’ the audience. |
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Your lyrics should have an intriguing meaning and message that people can relate too. When it comes to choosing your beat, |
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make sure it is catchy yet relevant to your words, so that when combined they make an explosion of sound everytime! |
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My first request is "I need a rap song about finding strength within yourself." |
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<|end_of_text|>' |
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""" |
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``` |
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## Training Details |
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### Training Procedure |
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The model was fine-tuned using the **Unsloth library** with LoRA adapters, enabling efficient training. Below are the hyperparameters used: |
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```python |
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args = TrainingArguments( |
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per_device_train_batch_size = 2, |
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gradient_accumulation_steps = 4, |
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warmup_steps = 5, |
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num_train_epochs = 8, |
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# max_steps = 60, |
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learning_rate = 2e-4, |
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fp16 = not is_bfloat16_supported(), |
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bf16 = is_bfloat16_supported(), |
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logging_steps = 1, |
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optim = "adamw_8bit", |
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weight_decay = 0.01, |
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lr_scheduler_type = "linear", |
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seed = 3407, |
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output_dir = "outputs", |
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report_to = "none", |
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) |
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``` |
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#### Hardware |
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- Trained on google colab with its T4 GPU |
