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---
license: apache-2.0
datasets:
- Rahulholla/stock-analysis
---
**Usage**
```
from llama_cpp import Llama
from typing import Optional
import time
from huggingface_hub import hf_hub_download
def generate_prompt(input_text: str, instruction: Optional[str] = None) -> str:
text = f"### Question: {input_text}\n\n### Answer: "
if instruction:
text = f"### Instruction: {instruction}\n\n{text}"
return text
# Set up the parameters
repo_id = "vdpappu/gemma2_stocks_analysis_gguf"
filename = "gemma2_stocks_analysis.gguf"
local_dir = "."
downloaded_file_path = hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
print(f"File downloaded to: {downloaded_file_path}")
# Load the model
llm = Llama(model_path=downloaded_file_path) #1 is thug
question = """Assume the role as a seasoned stock option analyst with a strong track record in dissecting intricate option data to discern valuable
insights into stock sentiment. Proficient in utilizing advanced statistical models and data visualization techniques to forecast
market trends and make informed trading decisions. Adept at interpreting option Greeks, implied volatility, .. """
prompt = generate_prompt(input_text=question)
start = time.time()
output = llm(prompt,
temperature=0.7,
top_p=0.9,
top_k=50,
repeat_penalty=1.5,
max_tokens=200,
stop=["Question:","<eos>"])
end = time.time()
print(f"Inference time: {end-start:.2f} seconds \n")
print(output['choices'][0]['text'])
``` |