rahulholla1/stock-analysis
Viewer • Updated • 948 • 15 • 4
How to use vdpappu/gemma2_stocks_analysis_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vdpappu/gemma2_stocks_analysis_gguf", filename="lora_stock_analysis.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
How to use vdpappu/gemma2_stocks_analysis_gguf with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vdpappu/gemma2_stocks_analysis_gguf # Run inference directly in the terminal: llama-cli -hf vdpappu/gemma2_stocks_analysis_gguf
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vdpappu/gemma2_stocks_analysis_gguf # Run inference directly in the terminal: llama-cli -hf vdpappu/gemma2_stocks_analysis_gguf
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf vdpappu/gemma2_stocks_analysis_gguf # Run inference directly in the terminal: ./llama-cli -hf vdpappu/gemma2_stocks_analysis_gguf
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf vdpappu/gemma2_stocks_analysis_gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf vdpappu/gemma2_stocks_analysis_gguf
docker model run hf.co/vdpappu/gemma2_stocks_analysis_gguf
How to use vdpappu/gemma2_stocks_analysis_gguf with Ollama:
ollama run hf.co/vdpappu/gemma2_stocks_analysis_gguf
How to use vdpappu/gemma2_stocks_analysis_gguf with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vdpappu/gemma2_stocks_analysis_gguf to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vdpappu/gemma2_stocks_analysis_gguf to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vdpappu/gemma2_stocks_analysis_gguf to start chatting
How to use vdpappu/gemma2_stocks_analysis_gguf with Docker Model Runner:
docker model run hf.co/vdpappu/gemma2_stocks_analysis_gguf
How to use vdpappu/gemma2_stocks_analysis_gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vdpappu/gemma2_stocks_analysis_gguf
lemonade run user.gemma2_stocks_analysis_gguf-{{QUANT_TAG}}lemonade list
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'])
We're not able to determine the quantization variants.