Update README.md

README.md

@@ -9,6 +9,7 @@ tags:
 - llama
 - trl
 base_model: unsloth/llama-3-8b-bnb-4bit
+datasets: gbharti/finance-alpaca
 ---
 
 # Uploaded  model
@@ -17,6 +18,62 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 - **License:** apache-2.0
 - **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.  
+A fine-tuned `unsloth/llama-3-8b-bnb-4bit` model on [gbharti/finance-alpaca](https://huggingface.co/datasets/gbharti/finance-alpaca) dataset. 
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# Model Usage
+
+Use the **unsloth** library to download and use the model. 
+
+```python
+from unsloth import FastLanguageModel
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "dmedhi/llama-3-personal-finance-8b-bnb-4bit",
+        max_seq_length = max_seq_length,
+        dtype = dtype,
+        load_in_4bit = load_in_4bit,
+    )
+    FastLanguageModel.for_inference(model)
+inputs = tokenizer(
+[
+    prompt.format(
+        "Which is better, Mutual fund or Fixed deposit?", # instruction
+        "", # input
+        "", # output
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) # play around with number of tokens for better results
+result = tokenizer.batch_decode(outputs)
+print(f"Response:\n{result[0]}")
+
+"""
+Response:
+<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context.
+Write a response that appropriately completes the request.
+
+### Instruction:
+If I buy a stock and hold will I get rich?
+
+### Input:
+
+### Response:
+I'm not sure what you mean by "get rich".  If you buy a stock and hold it for a long time, you will probably make money.
+If you buy a stock and hold it for a short time, you might make money, but you might also lose money.  It all depends on how
+"""
+```
+
+This model can also be used using the `AutoModelForPeftCausalLM` from **peft** library but it is very slow and not recommended.
+
+```python
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "dmedhi/llama-3-personal-finance-8b-bnb-4bit",
+    load_in_4bit = load_in_4bit,
+)
+tokenizer = AutoTokenizer.from_pretrained("lora_model")
+```
+
+For complete code and example, please refer to this [notebook](https://github.com/d1pankarmedhi/fine-tuning-llm/blob/main/llama3-personal-finance-FT.ipynb) which includes
+dataset preparation, training code and model inference example.