Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 language:
 - en
-license: apache-2.0
+license: cc-by-nc-4.0
 tags:
 - text-generation-inference
 - transformers
@@ -14,9 +14,55 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 # Uploaded  model
 
 - **Developed by:** anamikac2708
-- **License:** apache-2.0
+- **License:** cc-by-nc-4.0
 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library using open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+Model then converted Q4_K_M gguf using llama.cpp https://github.com/ggerganov/llama.cpp/. 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. -->
+1. ## Install llama-cpp-python:
+
+```
+  ! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python 
+```
+
+2. ## Run the model
+
+```
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')
+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 = tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
+
+llm = Llama.from_pretrained(
+    repo_id="anamikac2708/Llama3-8b-finetuned-investopedia-q4_k_m_gguf",
+    filename="*Q4_K_M.gguf",
+    verbose=False
+)
+
+output = llm(
+  prompt,
+  max_tokens=256,  # Generate up to 256 tokens
+  stop=["<|im_end|>"], 
+  echo=True,  # Whether to echo the prompt
+)
+
+print(output['choices'][0]['text'])
+```
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+Coming soon!
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking into ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
+
+## License
+
+Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0.