Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- gbharti/finance-alpaca
+language:
+- en
+library_name: transformers
+tags:
+- finance
+widget:
+- text: >-
+    user: Hypothetical, can taxes ever cause a net loss on otherwise-profitable stocks?
+
+    bot:
+  example_title: Hypothetical
+- text: >-
+    user: What are some signs that the stock market might crash?
+
+    bot:
+  example_title: Question 2
+- text: >-
+    user: Where should I be investing my money?
+
+    bot:
+  example_title: Question
+- text: >-
+    user: Is this headline positive or negative? Headline: Australian Tycoon
+    Forrest Shuts Nickel Mines After Prices Crash.
+
+    bot:
+  example_title: Sentiment analysis
+- text: >-
+    user: Aluminum price per KG is 50$. Forecast max: +1$ min:+0.3$. What should
+    be the current price of aluminum?
+
+    bot:
+  example_title: Forecast
+---
+
+# Fin-RWKV: Attention Free Financal Expert (WIP)
+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.
+
+## Use Cases
+- Sentiment analysis
+- Forecast
+- Product Pricing
+
+## Features
+- Attention-Free Architecture: Utilizes the RWKV (Recurrent Weighted Kernel-based) model, which bypasses the complexity of attention mechanisms while maintaining high performance.
+- Lower Costs: 10x to over a 100x+ lower inference cost, 2x to 10x lower training cost
+- Tinyyyy: Lightweight enough to run on CPUs in real-time bypassing the GPU - and is able to run on your laptop today
+- Finance-Specific Training: Trained on the gbharti/finance-alpaca dataset, ensuring that the model is finely tuned for financial data analysis.
+- Transformers Library Integration: Built on the popular 'transformers' library, ensuring easy integration with existing ML pipelines and applications.
+
+## How to use
+```py
+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)
+```
+
+## Competing Against
+| Name          | Param Count | Cost | Inference Cost |
+|---------------|-------------|------|----------------|
+| Fin-RWKV | 1B5 | $3 | Free on  HuggingFace 🤗 & Low-End CPU |
+| [BloombergGPT](https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/) | 50 Billion | $1.3 million | Enterprise GPUs |
+| [FinGPT](https://huggingface.co/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](https://arxiv.org/abs/2305.13048) |
+| Ret Net (Microsoft) | Research | O ( N ) | 6.7B | 100B (mixed) | [Paper](https://arxiv.org/abs/2307.08621) |
+| State Space (Stanford) | Prototype | O ( Log N ) | 355M | 15B (the pile, subset) | [Paper](https://arxiv.org/abs/2302.10866) |
+| Liquid (MIT) | Research | - | <1M | - | [Paper](https://arxiv.org/abs/2302.10866) |
+| Transformer Architecture (included for contrasting reference) | In Production | O ( N^2 ) | 800B (est) | 13T++ (est) | - |
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/631ea4247beada30465fa606/7vAOYsXH1vhTyh22o6jYB.png" width="500" alt="Inference computational cost vs. Number of tokens">
+
+## Stats for nerds
+### Training Config
+- n_epoch: 100
+- epoch_save_frequency: 10
+- batch_size: 5
+- ctx_len: 2000
+- T_MAX: 384
+- RWKV_FLOAT_MODE: fp16
+- RWKV_DEEPSPEED: 0 
+
+### Loss
+<img src="https://cdn-uploads.huggingface.co/production/uploads/631ea4247beada30465fa606/NvPKCBlbVhiVeeMpUAv2C.png" width="500" alt="Loss">
+
+_Note: Needs more data and training, testing purposes only. Not recomended for production level deployment._
+[Presentation](https://docs.google.com/presentation/d/1vNQ8Y5wwR0WXlO60fsXjkru5R9I0ZgykTmgag0B3Ato/edit?usp=sharing)