Fin-RWKV-V1
Collection
Attention free financial expert modal - RWKV V4
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6 items
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Updated
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1
Fin-RWKV is a cutting-edge, attention-free model designed specifically for financial analysis and prediction. Developed as part of a MindsDB Hackathon, this model leverages the simplicity and efficiency of the RWKV architecture to process financial data, providing insights and forecasts with remarkable accuracy. Fin-RWKV is tailored for professionals and enthusiasts in the finance sector who seek to integrate advanced deep learning techniques into their financial analyses.
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import torch
tokenizer = AutoTokenizer.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")
model = AutoModelForCausalLM.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")
prompt = "user: Is this headline positive or negative? Headline: Australian Tycoon Forrest Shuts Nickel Mines After Prices Crash\nbot:"
# Tokenize the input
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate a response
output = model.generate(input_ids, max_length=333, num_return_sequences=1)
# Decode the output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
Name | Param Count | Cost | Inference Cost |
---|---|---|---|
Fin-RWKV | 1B5 | $3 | Free on HuggingFace 🤗 & Low-End CPU |
BloombergGPT | 50 Billion | $1.3 million | Enterprise GPUs |
FinGPT | 7 Bilion | $302.4 | Consumer GPUs |
Architecture | Status | Compute Efficiency | Largest Model | Trained Token | Link |
---|---|---|---|---|---|
(Fin)RWKV | In Production | O ( N ) | 14B | 500B++ (the pile+) | Paper |
Ret Net (Microsoft) | Research | O ( N ) | 6.7B | 100B (mixed) | Paper |
State Space (Stanford) | Prototype | O ( Log N ) | 355M | 15B (the pile, subset) | Paper |
Liquid (MIT) | Research | - | <1M | - | Paper |
Transformer Architecture (included for contrasting reference) | In Production | O ( N^2 ) | 800B (est) | 13T++ (est) | - |
Note: Needs more data and training, testing purposes only. Not recomended for production level deployment. Presentation