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---
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language:
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- en
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license: apache-2.0
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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tags:
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- MSME
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- India
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- enterprise
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- peft
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- lora
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library_name: peft
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---
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# TinyLlama MSME India
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Fine-tuned TinyLlama-1.1B for Indian MSME enterprise data.
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## Quick Start
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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base_model = AutoModelForCausalLM.from_pretrained(
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"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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device_map="auto",
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torch_dtype=torch.float16
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)
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model = PeftModel.from_pretrained(base_model, "samarthbhadane/tinyllama-msme-india")
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tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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prompt = """<|system|>
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You are a helpful assistant that provides information about MSME enterprises in India.</s>
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<|user|>
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Tell me about PARAG MASALA UDYOG</s>
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<|assistant|>
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.3)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training
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- **Training Data:** 10,000 MSME records
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- **Steps:** 2,000
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- **Final Loss:** 0.516 (train) / 0.530 (val)
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- **LoRA Rank:** 8
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- **GPU:** NVIDIA RTX A4000
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## License
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Apache 2.0
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