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README.md

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+---
+language:
+- en
+- hi
+license: llama2
+tags:
+- multilingual
+- instruction-tuning
+- llama2
+datasets:
+- ai4bharat/indic-instruct-data-v0.1
+---
+
+# Airavata
+
+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) 
+which is a collection of instruction datasets (Anudesh, wikiHow, Flan v2, Dolly, Anthropic-HHH, OpenAssistant v1, and LymSys-Chat). 
+Please check the corresponding huggingface dataset card for more details.
+
+This was trained as part of the technical report [Airavata: Introducing Hindi Instruction-tuned LLM](https://arxiv.org/abs/2401.15006).
+The codebase used to train and evaluate this model can be found at [https://github.com/AI4Bharat/IndicInstruct](https://github.com/AI4Bharat/IndicInstruct).
+
+## Usage
+
+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.
+
+## Input Format
+
+The model is trained to use the chat format similar to [open-instruct code repository](https://github.com/allenai/open-instruct) (note the newlines):
+```
+<|user|>
+Your message here!
+<|assistant|>
+```
+
+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.**
+
+## Hyperparameters
+
+We fine-tune OpenHathi base model on the aforementioned IndicInstruct dataset with LoRA. The hyperparameters for the LoRA fine-tuning are listed below:
+- LoRA Rank: 16
+- LoRA alpha: 32
+- LoRA Dropout: 0.05
+- LoRA Target Modules: ["q_proj", "v_proj", "k_proj", "down_proj", "gate_proj", "up_proj"]
+- Epochs: 4
+- Learning rate: 5e-4
+- Batch Size: 128
+- Floating Point Precision: bfloat16
+
+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.
+
+## Example
+
+```python3
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True):
+    formatted_text = ""
+    for message in messages:
+        if message["role"] == "system":
+            formatted_text += "<|system|>\n" + message["content"] + "\n"
+        elif message["role"] == "user":
+            formatted_text += "<|user|>\n" + message["content"] + "\n"
+        elif message["role"] == "assistant":
+            formatted_text += "<|assistant|>\n" + message["content"].strip() + eos + "\n"
+        else:
+            raise ValueError(
+                "Tulu chat template only supports 'system', 'user' and 'assistant' roles. Invalid role: {}.".format(
+                    message["role"]
+                )
+            )
+    formatted_text += "<|assistant|>\n"
+    formatted_text = bos + formatted_text if add_bos else formatted_text
+    return formatted_text
+
+
+def inference(input_prompts, model, tokenizer):
+    input_prompts = [
+        create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False)
+        for input_prompt in input_prompts
+    ]
+
+    encodings = tokenizer(input_prompts, padding=True, return_tensors="pt")
+    encodings = encodings.to(device)
+
+    with torch.inference_mode():
+        outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=250)
+
+    output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
+
+    input_prompts = [
+        tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts
+    ]
+    output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)]
+    return output_texts
+
+
+model_name = "ai4bharat/Airavata"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+tokenizer.pad_token = tokenizer.eos_token
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
+
+input_prompts = [
+    "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं।",
+    "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं और उनका वर्णन करें।",
+]
+outputs = inference(input_prompts, model, tokenizer)
+print(outputs)
+```
+
+## Citation
+
+```bibtex
+@article{gala2024airavata,
+  title   = {Airavata: Introducing Hindi Instruction-tuned LLM},
+  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},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: 2401.15006}
+}
+```

added_tokens.json

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+{
+  "<pad>": 48064
+}

config.json

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+{
+  "_name_or_path": "ai4bharat/Airavata",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.1",
+  "use_cache": true,
+  "vocab_size": 48065
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.01,
+  "top_p": 0.9,
+  "transformers_version": "4.37.1"
+}

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