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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ - hi
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+ license: llama2
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+ tags:
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+ - multilingual
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+ - instruction-tuning
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+ - llama2
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+ datasets:
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+ - ai4bharat/indic-instruct-data-v0.1
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+ ---
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+
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+ # Airavata
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+
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+ This model is a 7B [OpenHathi](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base) model finetuned on [IndicInstruct dataset](https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1)
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+ which is a collection of instruction datasets (Anudesh, wikiHow, Flan v2, Dolly, Anthropic-HHH, OpenAssistant v1, and LymSys-Chat).
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+ Please check the corresponding huggingface dataset card for more details.
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+
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+ This was trained as part of the technical report [Airavata: Introducing Hindi Instruction-tuned LLM](https://arxiv.org/abs/2401.15006).
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+ The codebase used to train and evaluate this model can be found at [https://github.com/AI4Bharat/IndicInstruct](https://github.com/AI4Bharat/IndicInstruct).
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+
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+ ## Usage
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+
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+ Clone [https://github.com/AI4Bharat/IndicInstruct](https://github.com/AI4Bharat/IndicInstruct) and install the required dependencies. Then download or clone this model to the same machine.
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+
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+ ## Input Format
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+
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+ The model is trained to use the chat format similar to [open-instruct code repository](https://github.com/allenai/open-instruct) (note the newlines):
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+ ```
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+ <|user|>
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+ Your message here!
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+ <|assistant|>
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+ ```
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+
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+ For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
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+
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+ ## Hyperparameters
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+
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+ We fine-tune OpenHathi base model on the aforementioned IndicInstruct dataset with LoRA. The hyperparameters for the LoRA fine-tuning are listed below:
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+ - LoRA Rank: 16
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+ - LoRA alpha: 32
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+ - LoRA Dropout: 0.05
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+ - LoRA Target Modules: ["q_proj", "v_proj", "k_proj", "down_proj", "gate_proj", "up_proj"]
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+ - Epochs: 4
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+ - Learning rate: 5e-4
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+ - Batch Size: 128
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+ - Floating Point Precision: bfloat16
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+
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+ We recommend the readers to check out [our official blog post](https://ai4bharat.github.io/airavata) for more details on the model training, ablations and evaluation results.
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+
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+ ## Example
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+
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+ ```python3
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+
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+
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+ def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True):
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+ formatted_text = ""
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+ for message in messages:
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+ if message["role"] == "system":
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+ formatted_text += "<|system|>\n" + message["content"] + "\n"
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+ elif message["role"] == "user":
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+ formatted_text += "<|user|>\n" + message["content"] + "\n"
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+ elif message["role"] == "assistant":
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+ formatted_text += "<|assistant|>\n" + message["content"].strip() + eos + "\n"
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+ else:
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+ raise ValueError(
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+ "Tulu chat template only supports 'system', 'user' and 'assistant' roles. Invalid role: {}.".format(
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+ message["role"]
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+ )
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+ )
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+ formatted_text += "<|assistant|>\n"
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+ formatted_text = bos + formatted_text if add_bos else formatted_text
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+ return formatted_text
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+
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+
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+ def inference(input_prompts, model, tokenizer):
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+ input_prompts = [
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+ create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False)
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+ for input_prompt in input_prompts
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+ ]
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+
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+ encodings = tokenizer(input_prompts, padding=True, return_tensors="pt")
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+ encodings = encodings.to(device)
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+
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+ with torch.inference_mode():
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+ outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=250)
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+
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+ output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
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+
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+ input_prompts = [
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+ tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts
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+ ]
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+ output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)]
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+ return output_texts
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+
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+
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+ model_name = "ai4bharat/Airavata"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
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+ tokenizer.pad_token = tokenizer.eos_token
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+ model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
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+
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+ input_prompts = [
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+ "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं।",
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+ "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं और उनका वर्णन करें।",
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+ ]
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+ outputs = inference(input_prompts, model, tokenizer)
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+ print(outputs)
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+ ```
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{gala2024airavata,
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+ title = {Airavata: Introducing Hindi Instruction-tuned LLM},
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+ author = {Jay Gala and Thanmay Jayakumar and Jaavid Aktar Husain and Aswanth Kumar M and Mohammed Safi Ur Rahman Khan and Diptesh Kanojia and Ratish Puduppully and Mitesh M. Khapra and Raj Dabre and Rudra Murthy and Anoop Kunchukuttan},
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+ year = {2024},
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+ journal = {arXiv preprint arXiv: 2401.15006}
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+ }
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+ ```
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+ {
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+ "<pad>": 48064
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "ai4bharat/Airavata",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 11008,
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+ "max_position_embeddings": 4096,
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+ "model_type": "llama",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.37.1",
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+ "use_cache": true,
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+ "vocab_size": 48065
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+ }
generation_config.json ADDED
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+ {
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+ "transformers_version": "4.37.1"
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