Update README.md

README.md

@@ -1,6 +1,58 @@
 ---
 library_name: peft
 ---
+
+# FinGPT_v3.3 
+
+## Model info
+- Base model:  Llama2-13B
+- Training method:  Instruction Fine-tuning + LoRA + 8bit
+- Task:  Sentiment Analysis
+
+
+## Try the model
+``` python
+from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast
+from peft import PeftModel  # 0.5.0
+
+# Load Models
+base_model = "NousResearch/Llama-2-13b-hf" 
+peft_model = "oliverwang15/FinGPT_v33_Llama2_13B_Sentiment_Instruction_LoRA_FT_8bit"
+tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = "cuda:0", load_in_8bit = True,)
+model = PeftModel.from_pretrained(model, peft_model)
+model = model.eval()
+
+# Make prompts
+prompt = [
+'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}
+Input: FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is aggressively pursuing its growth strategy by increasingly focusing on technologically more demanding HDI printed circuit boards PCBs .
+Answer: ''',
+'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}
+Input: According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .
+Answer: ''',
+'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}
+Input: A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser .
+Answer: ''',
+]
+
+# Generate results
+tokens = tokenizer(prompt, return_tensors='pt', padding=True, max_length=512)
+res = model.generate(**tokens, max_length=512)
+res_sentences = [tokenizer.decode(i) for i in res]
+out_text = [o.split("Answer: ")[1] for o in res_sentences]
+
+# show results
+for sentiment in out_text:
+    print(sentiment)
+
+# Output:    
+# positive
+# neutral
+# negative
+```
+
 ## Training procedure