TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Deepmoney 34B 200K Base - GGUF
- Model creator: triad party
- Original model: Deepmoney 34B 200K Base
Description
This repo contains GGUF format model files for triad party's Deepmoney 34B 200K Base.
These files were quantised using hardware kindly provided by Massed Compute.
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 incomplete list of clients and libraries that are known to support GGUF:
- llama.cpp. The source project for GGUF. Offers a CLI and a server option.
- text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
- KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
- GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
- LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
- Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
- llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
- candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
- ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
- triad party's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: None
{prompt}
Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
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 |
|---|---|---|---|---|---|
| deepmoney-34b-200k-base.Q2_K.gguf | Q2_K | 2 | 12.77 GB | 15.27 GB | smallest, significant quality loss - not recommended for most purposes |
| deepmoney-34b-200k-base.Q3_K_S.gguf | Q3_K_S | 3 | 14.96 GB | 17.46 GB | very small, high quality loss |
| deepmoney-34b-200k-base.Q3_K_M.gguf | Q3_K_M | 3 | 16.65 GB | 19.15 GB | very small, high quality loss |
| deepmoney-34b-200k-base.Q3_K_L.gguf | Q3_K_L | 3 | 18.14 GB | 20.64 GB | small, substantial quality loss |
| deepmoney-34b-200k-base.Q4_0.gguf | Q4_0 | 4 | 19.47 GB | 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| deepmoney-34b-200k-base.Q4_K_S.gguf | Q4_K_S | 4 | 19.60 GB | 22.10 GB | small, greater quality loss |
| deepmoney-34b-200k-base.Q4_K_M.gguf | Q4_K_M | 4 | 20.66 GB | 23.16 GB | medium, balanced quality - recommended |
| deepmoney-34b-200k-base.Q5_0.gguf | Q5_0 | 5 | 23.71 GB | 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| deepmoney-34b-200k-base.Q5_K_S.gguf | Q5_K_S | 5 | 23.71 GB | 26.21 GB | large, low quality loss - recommended |
| deepmoney-34b-200k-base.Q5_K_M.gguf | Q5_K_M | 5 | 24.32 GB | 26.82 GB | large, very low quality loss - recommended |
| deepmoney-34b-200k-base.Q6_K.gguf | Q6_K | 6 | 28.21 GB | 30.71 GB | very large, extremely low quality loss |
| deepmoney-34b-200k-base.Q8_0.gguf | Q8_0 | 8 | 36.54 GB | 39.04 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/deepmoney-34b-200k-base-GGUF and below it, a specific filename to download, such as: deepmoney-34b-200k-base.Q4_K_M.gguf.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
Then you can download any individual model file to the current directory, at high speed, with a command like this:
huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage (click to read)
You can also download multiple files at once with a pattern:
huggingface-cli download TheBloke/deepmoney-34b-200k-base-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.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.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 or later.
./main -ngl 35 -m deepmoney-34b-200k-base.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 200000 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
For other parameters and how to use them, please refer to the llama.cpp documentation
How to run in text-generation-webui
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.
How to run from Python code
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
How to load this model in Python code, using llama-cpp-python
For full documentation, please see: llama-cpp-python docs.
First install the package
Run one of the following commands, according to your system:
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
Simple llama-cpp-python example code
from llama_cpp import Llama
# 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 = Llama(
model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", # Download the model file first
n_ctx=200000, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute
Thanks to the chirper.ai team!
Thanks to Clay from gpus.llm-utils.org!
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: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: triad party's Deepmoney 34B 200K Base
Deepmoney
Introducing Greed in the Seven Deadly Sins series of models.
- Full-para pre-training on Yi-34b
- High-quality research reports
- High-end cleaning process
1. What do I want to do?
Most of the current so-called financial models are mostly trained on public knowledge, but in the actual financial field, these public knowledge are often seriously insufficient for the current market interpretability. If you are interested, you can learn about the various propositions of Keynes, Friedman and even current behavioral finance. According to my observation, most financial models cannot make investment judgments. Because they are trained on ordinary textbooks, entry-level analyst exams, and even company public reports. I think this is of very little value for the investment.
You can think I'm joking, but the fact is that the logic of many subjective analysts may not be as rigorous as that of large models of 34b and above (excluding those excellent ones, of course). The market is changing every moment, with a lot of news and massive data in real time. For most retail investors, instead of waiting for a crappy analyst to write a report, why not use a large model to make a pipeline?
In my plan, this model is the base model of this process. In my plan, models such as information collector, target judge, qualitative analyst, quantitative analyst, and data extractor are all part of this process. . But the model itself is undoubtedly important to master a large number of qualitative and quantitative methods. That's why this model was born.
2. About the data
As I just said, a lot of public knowledge has some questionable validity - but that doesn't mean it's wrong. The theoretical support behind many research methods in research reports also relies on this knowledge. So in my training, I picked up some college textbooks and some professional books. Not a lot of quantity but good quality. In addition, I selected a large number of research report data from 2019 to December 2023 - these reports are issued by a variety of publishers, including traditional brokers and professional research institutions. Most of them are paid and only available to institutions. But I got them anyway through various means.
If you have read research reports, especially high-quality ones, you will find that research reports are all subjective judgment + quantitative analysis, and data support in quantitative analysis is crucial to the entire logical chain. In order to extract this data (most of them are in the form of graphs or tables), I tried a lot of multi-modal models, and the process was very painful. The conclusion is that cog-agent and emu2 are very effective for this kind of tasks. In order to better extract information, I created a process that summarizes the context of research reports as part of the prompt.
Finally, I made a blend of the data. General data is not included because it is just for greed. Moreover, the knowledge in industry research reports is comprehensive enough.
3. About training
Raw text, full parameter training. The base uses long context yi-34b-200k. This is necessary to complete and understand an in-depth report.
Of course, I also did a sft. This is the analyzer in my process – I haven’t broken down the qualitative and quantitative analysis yet, but I’m already blown away by how well it works.
More:
More technical details and evals coming soon……
1. 我想干什么？
当下大多数所谓的金融模型大多在公开知识上进行训练，但在实际的金融领域，这些公开知识对当前的市场可解释性往往严重不足。如果您感兴趣，可以了解一下凯恩斯、弗里德曼乃至当下行为金融学的各类主张。而据我观察，大多数金融模型无法对投资进行判断。因为它们都是在普通的教材、入门的分析师考试，乃至公司的公开报告上训练。我认为这对于投资的价值非常小。 你可以当我开玩笑，但事实是很多主观分析师的逻辑性可能还不如34b及以上的大模型来的严谨（当然不包括那些优秀的）。而每时每刻，市场都在变化，大量的新闻，海量的数据都是实时的，对于大多数散户们，与其等待蹩脚的分析师写出报告，为什么不用大模型制作一套pipeline呢？ 在我的计划中，该模型是这套流程的基座模型，在我的计划中，信息搜集者、标的判断者、定性分析者定性分析者、定量分析者、数据提取者等模型都是该流程的一部分。但模型本身掌握大量的定性和定量方法毫无疑问是重要的。这就是这个模型诞生的理由。
2. 关于数据：
正如我刚才所说，很多公开知识的有效性都有些问题——但这并不意味着它们是错误的。在研报中很多研究方法背后的理论支撑也依赖这些知识。所以在我的训练中，我挑选了一些大学教材和一些专业书籍。数量不是很多但质量还不错。另外，我挑选了在2019-2023年12月的大量研究报告数据——这些报告的发布者多种多样，有传统的broke，也有专业研究机构。他们中的大多数是付费的，而且只对机构提供。但无论如何我通过各种各样的手段获取了它们。
如果你看过研报，尤其是高质量的那些，你会发现研报都是主观判断+定量分析，而定量分析中的数据支撑对于整个逻辑链条至关重要。为了提取这些数据（他们中的大多数以图形或者表格的形式出现），我尝试了很多多模态模型，过程非常痛苦，结论是cog-agent和emu2对于这类任务很有效。为了更好的提取信息，我制作了一套从研报上下文总结作为prompt一部分的流程。
最后，我把这些数据做了一个混合。并没有放入通识数据, 因为它就是为了greed而生的。而且行业研报中的知识足够全。
3：关于训练：
raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对于完成理解一篇深度报告是必须的。
当然，我也做了一次sft。这是我的流程中的分析者——目前还没有细分定性和定量分析，但它的效果已经让我大吃一惊了。
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