anamikac2708
/

Llama3-8b-finetuned-investopedia-Lora-Adapters

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+language:
+- en
+license: cc-by-nc-4.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- lora
+base_model: unsloth/llama-3-8b-bnb-4bit
 ---
+# Uploaded  model
+- **Developed by:** anamikac2708
+- **License:** cc-by-nc-4.0
+- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
+This are lora adapeters that are trained on top of llama3-8B model using 2x faster [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library using our open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team
+This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.
 ## How to Get Started with the Model
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+You need to merge the adapter with the base model and infer it using PEFT or Unsloth library.
+Please find an example below using Unsloth:
+```
+import torch
+from unsloth import FastLanguageModel
+from transformers import AutoTokenizer, pipeline
+model_id='FinLang/investopedia_chat_model'
+max_seq_length=2048
+model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "anamikac2708/Llama3-8b-finetuned-investopedia-Merged-FP16", # YOUR MODEL YOU USED FOR TRAINING
+        max_seq_length = max_seq_length,
+        dtype = torch.bfloat16
+    )
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+example = [{'content': 'You are a financial expert and you can answer any questions related to finance. You will be given a context and a question. Understand the given context and\n        try to answer. Users will ask you questions in English and you will generate answer based on the provided CONTEXT.\n        CONTEXT:\n        D. in Forced Migration from the University of the Witwatersrand (Wits) in Johannesburg, South Africa; A postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University (IGNOU) in New Delhi, India; A Masters of International Affairs at Columbia University; A BA from Barnard College at Columbia University\n', 'role': 'system'}, {'content': ' In which universities did the individual obtain their academic qualifications?\n', 'role': 'user'}, {'content': ' University of the Witwatersrand (Wits) in Johannesburg, South Africa; Indira Gandhi National Open University (IGNOU) in New Delhi, India; Columbia University; Barnard College at Columbia University.', 'role': 'assistant'}]
+prompt = pipe.tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.1, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
+print(f"Query:\n{example[1]['content']}")
+print(f"Context:\n{example[0]['content']}")
+print(f"Original Answer:\n{example[2]['content']}")
+print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")
+```
+## License
+Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0.