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--- |
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tags: |
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- Code-Generation |
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- autotrain |
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- text-generation |
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- Llama2 |
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- Pytorch |
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- PEFT |
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- QLoRA |
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- code |
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- coding |
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pipeline_tag: text-generation |
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widget: |
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- text: Write a program that add five numbers |
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- text: Write a python code for reading multiple images |
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- text: Write a python code for the name Ahmed to be in a reversed order |
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datasets: |
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- AhmedSSoliman/CodeSearchNet |
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- AhmedSSoliman/CodeSearchNet-Python |
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--- |
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# LlaMa2-CodeGen |
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This model is [**LlaMa2-7b**](https://huggingface.co/meta-llama/Llama-2-7b) which is fine-tuned on the [**CodeSearchNet dataset**](https://github.com/github/CodeSearchNet) by using the method [**QLoRA**](https://github.com/artidoro/qlora) with [PEFT](https://github.com/huggingface/peft) library. |
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# Model Trained on Google Colab Pro Using AutoTrain, PEFT and QLoRA |
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[![Open in Colab][Colab Badge]][RDP Notebook] |
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# You can load the LlaMa2-CodeGen model on google colab. |
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### Example |
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```py |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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peft_model_id = "AhmedSSoliman/Llama2-CodeGen-PEFT-QLoRA" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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def create_prompt(instruction): |
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system = "You are using the Llam2-CodeGen model, a coding assistant that will help the user to resolve the following instruction:\n" |
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instruction = "### Input: " + instruction |
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return system + "\n" + instruction + "\n\n" + "### Response:" + "\n" |
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def generate( |
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instruction, |
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max_new_tokens=128, |
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temperature=0.1, |
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top_p=0.75, |
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top_k=40, |
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num_beams=4, |
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**kwargs, |
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): |
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prompt = create_prompt(instruction) |
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print(prompt) |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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#input_ids = inputs["input_ids"].to("cuda") |
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#attention_mask = inputs["attention_mask"].to("cuda") |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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#input_ids=input_ids, |
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#attention_mask=attention_mask, |
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**inputs, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=max_new_tokens, |
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early_stopping=True |
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) |
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generated_response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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stop_output = "### Input" |
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gen_response = (generated_response.split(stop_output))[0] |
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#s = generation_output.sequences[0] |
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#output = tokenizer.decode(s, skip_special_tokens=True) |
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#stop_output = "### Input" |
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#gen_response = (output.split(stop_output))[0] |
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#return output.split("### Response:")[1].lstrip("\n") |
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return gen_response |
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instruction = """ |
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Write a python code for the name Ahmed to be in a reversed order |
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""" |
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print(generate(instruction)) |
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``` |
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[Colab Badge]: https://colab.research.google.com/assets/colab-badge.svg |
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[License-Badge]: https://img.shields.io/badge/License-MIT-blue.svg |
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[RDP Issues]: https://img.shields.io/github/issues/PradyumnaKrishna/Colab-Hacks/Colab%20RDP?label=Issues |
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[RDP Notebook]: https://colab.research.google.com/drive/18sAFC7msV0gJ24wn5gl41nU0QRynfLqG?usp=sharing |
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[Code Issues]: https://img.shields.io/github/issues/PradyumnaKrishna/Colab-Hacks/Code%20Server?label=Issues |
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[Code Notebook]: https://colab.research.google.com/drive/18sAFC7msV0gJ24wn5gl41nU0QRynfLqG?usp=sharing |
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