---
license: other
tags:
- finance
- llm
- llama
- trading
datasets:
- bavest/fin-llama-dataset
model_name: Fin Llama 33B
base_model: bavest/fin-llama-33b-merged
inference: false
model_creator: Bavest
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
# Fin Llama 33B - GGUF
- Model creator: [Bavest](https://huggingface.co./bavest)
- Original model: [Fin Llama 33B](https://huggingface.co./bavest/fin-llama-33b-merged)
## Description
This repo contains GGUF format model files for [Bavest's Fin Llama 33B](https://huggingface.co./bavest/fin-llama-33b-merged).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co./TheBloke/fin-llama-33B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co./TheBloke/fin-llama-33B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co./TheBloke/fin-llama-33B-GGUF)
* [Bavest's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co./bavest/fin-llama-33b-merged)
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
Click to see details
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [fin-llama-33b.Q2_K.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q2_K.gguf) | Q2_K | 2 | 13.50 GB| 16.00 GB | smallest, significant quality loss - not recommended for most purposes |
| [fin-llama-33b.Q3_K_S.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.06 GB| 16.56 GB | very small, high quality loss |
| [fin-llama-33b.Q3_K_M.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q3_K_M.gguf) | Q3_K_M | 3 | 15.76 GB| 18.26 GB | very small, high quality loss |
| [fin-llama-33b.Q3_K_L.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q3_K_L.gguf) | Q3_K_L | 3 | 17.28 GB| 19.78 GB | small, substantial quality loss |
| [fin-llama-33b.Q4_0.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q4_0.gguf) | Q4_0 | 4 | 18.36 GB| 20.86 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [fin-llama-33b.Q4_K_S.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q4_K_S.gguf) | Q4_K_S | 4 | 18.44 GB| 20.94 GB | small, greater quality loss |
| [fin-llama-33b.Q4_K_M.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q4_K_M.gguf) | Q4_K_M | 4 | 19.62 GB| 22.12 GB | medium, balanced quality - recommended |
| [fin-llama-33b.Q5_0.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q5_0.gguf) | Q5_0 | 5 | 22.40 GB| 24.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [fin-llama-33b.Q5_K_S.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q5_K_S.gguf) | Q5_K_S | 5 | 22.40 GB| 24.90 GB | large, low quality loss - recommended |
| [fin-llama-33b.Q5_K_M.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q5_K_M.gguf) | Q5_K_M | 5 | 23.05 GB| 25.55 GB | large, very low quality loss - recommended |
| [fin-llama-33b.Q6_K.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q6_K.gguf) | Q6_K | 6 | 26.69 GB| 29.19 GB | very large, extremely low quality loss |
| [fin-llama-33b.Q8_0.gguf](https://huggingface.co./TheBloke/fin-llama-33B-GGUF/blob/main/fin-llama-33b.Q8_0.gguf) | Q8_0 | 8 | 34.57 GB| 37.07 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/fin-llama-33B-GGUF and below it, a specific filename to download, such as: fin-llama-33b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/fin-llama-33B-GGUF fin-llama-33b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/fin-llama-33B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co./docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/fin-llama-33B-GGUF fin-llama-33b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m fin-llama-33b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p ` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/fin-llama-33B-GGUF", model_file="fin-llama-33b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: Bavest's Fin Llama 33B
# FIN-LLAMA
> Efficient Finetuning of Quantized LLMs for Finance
[Adapter Weights](https://huggingface.co./bavest/fin-llama-33b-merged)
| [Dataset](https://huggingface.co./datasets/bavest/fin-llama-dataset)
## Installation
To load models in 4bits with transformers and bitsandbytes, you have to install accelerate and transformers from source
and make sure you have the latest version of the bitsandbytes library (0.39.0).
```bash
pip3 install -r requirements.txt
```
### Other dependencies
If you want to finetune the model on a new instance. You could run
the `setup.sh` to install the python and cuda package.
```bash
bash scripts/setup.sh
```
## Finetuning
```bash
bash script/finetune.sh
```
## Usage
Quantization parameters are controlled from the `BitsandbytesConfig`
- Loading in 4 bits is activated through `load_in_4bit`
- The datatype used for the linear layer computations with `bnb_4bit_compute_dtype`
- Nested quantization is activated through `bnb_4bit_use_double_quant`
- The datatype used for qunatization is specified with `bnb_4bit_quant_type`. Note that there are two supported
quantization datatypes `fp4` (four bit float) and `nf4` (normal four bit float). The latter is theoretically optimal
for normally distributed weights and we recommend using `nf4`.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
pretrained_model_name_or_path = "bavest/fin-llama-33b-merge"
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path,
load_in_4bit=True,
device_map='auto',
torch_dtype=torch.bfloat16,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
question = "What is the market cap of apple?"
input = "" # context if needed
prompt = f"""
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's question.
'### Instruction:\n{question}\n\n### Input:{input}\n""\n\n### Response:
"""
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('cuda:0')
with torch.no_grad():
generated_ids = model.generate(
input_ids,
do_sample=True,
top_p=0.9,
temperature=0.8,
max_length=128
)
generated_text = tokenizer.decode(
[el.item() for el in generated_ids[0]], skip_special_tokens=True
)
```
## Dataset for FIN-LLAMA
The dataset is released under bigscience-openrail-m.
You can find the dataset used to train FIN-LLAMA models on HF
at [bavest/fin-llama-dataset](https://huggingface.co./datasets/bavest/fin-llama-dataset).
## Known Issues and Limitations
Here a list of known issues and bugs. If your issue is not reported here, please open a new issue and describe the
problem.
See [QLORA](https://github.com/artidoro/qlora) for any other limitations.
1. 4-bit inference is slow. Currently, our 4-bit inference implementation is not yet integrated with the 4-bit matrix
multiplication
2. Currently, using `bnb_4bit_compute_type='fp16'` can lead to instabilities.
3. Make sure that `tokenizer.bos_token_id = 1` to avoid generation issues.
## Acknowledgements
We also thank Meta for releasing the LLaMA models without which this work would not have been possible.
This repo builds on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
, [QLORA](https://github.com/artidoro/qlora), [Chinese-Guanaco](https://github.com/jianzhnie/Chinese-Guanaco/tree/main)
and [LMSYS FastChat](https://github.com/lm-sys/FastChat) repos.
## License and Intended Use
We release the resources associated with QLoRA finetuning in this repository under GLP3 license. In addition, we release the FIN-LLAMA model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models.
## Prompts
### Act as an Accountant
> I want you to act as an accountant and come up with creative ways to manage finances. You'll need to consider budgeting, investment strategies and risk management when creating a financial plan for your client. In some cases, you may also need to provide advice on taxation laws and regulations in order to help them maximize their profits. My first suggestion request is “Create a financial plan for a small business that focuses on cost savings and long-term investments".
## Paged Optimizer
You can access the paged optimizer with the argument --optim paged_adamw_32bit
## Cite
```tex
@misc{Fin-LLAMA,
author = {William Todt, Ramtin Babaei, Pedram Babaei},
title = {Fin-LLAMA: Efficient Finetuning of Quantized LLMs for Finance},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Bavest/fin-llama}},
}
```