deepmoney-34b-200k-base
Maintainer: TriadParty
50
🔄
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
deepmoney-34b-200k-base is a large language model created by TriadParty that has been trained on a high-quality dataset of financial research reports and other related data. It is part of TriadParty's "Seven Deadly Sins" series of models, with this model specifically focused on the sin of Greed. The model has undergone a thorough cleaning process and extensive pretraining to build broad financial knowledge and capabilities beyond what is typically found in public datasets.
Compared to similar models like deepsex-34b and WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ, deepmoney-34b-200k-base is specifically designed for financial applications and decision-making, rather than more general language tasks.
Model inputs and outputs
Inputs
- Plain text prompts related to financial analysis, investment decisions, or other business/economic topics
Outputs
- High-quality, researched responses providing insights, recommendations, or analysis on the given financial prompt
- Potential for generation of financial reports, investment theses, or other business-oriented content
Capabilities
deepmoney-34b-200k-base is capable of engaging in a wide range of financial and business-focused tasks beyond what typical language models can handle. It has been trained on a vast corpus of industry-specific knowledge, allowing it to provide nuanced and well-reasoned responses on topics like investment strategies, market trends, and business decision-making.
For example, the model could be used to generate personalized investment recommendations, analyze financial statements, or produce detailed research reports on economic sectors or individual companies. Its strong grasp of both qualitative and quantitative financial methods sets it apart from models trained solely on public datasets.
What can I use it for?
With its specialized financial knowledge and capabilities, deepmoney-34b-200k-base could be a valuable tool for a variety of business and investment-related applications. Some potential use cases include:
- Providing investment advice and portfolio management for retail investors
- Automating the production of high-quality financial research reports
- Assisting financial analysts and advisors with market analysis and investment decisions
- Generating personalized business plans, financial forecasts, or strategic recommendations
- Enhancing financial decision-making and risk management for enterprises
By leveraging the model's extensive training on industry-specific data and methods, users can unlock new efficiencies and insights within their financial workflows.
Things to try
One key aspect of deepmoney-34b-200k-base is its ability to go beyond the typical "public knowledge" that many financial models are limited to. By incorporating a diverse range of high-quality research reports and professional sources, the model can provide more nuanced and practical guidance on investment strategies and market dynamics.
To take full advantage of the model's capabilities, users could try prompting it with specific investment scenarios or business challenges, and see how it responds with tailored recommendations and analysis. Prompts could cover a wide range of topics, such as evaluating a particular stock or sector, optimizing a portfolio, or developing a growth strategy for a company.
Additionally, users may want to experiment with prompts that combine financial knowledge with other domains, such as using the model to generate business plans that incorporate relevant technological, regulatory, or macroeconomic factors. This could help uncover novel insights and applications that go beyond traditional financial modeling.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
⚙️
deepsex-34b
202
deepsex-34b is an AI model developed by TriadParty, a prominent AI researcher. While the platform did not provide a detailed description, this model is part of a family of similar text-to-image models like NSFW_13B_sft, Hentai-Diffusion, and HentaiDiffusion. These models specialize in generating explicit, adult-oriented visual content. Model inputs and outputs The deepsex-34b model takes text prompts as input and generates corresponding images. The text prompts can describe specific sexual acts, characters, or scenarios, which the model then renders visually. Inputs Text prompts describing explicit sexual content Outputs Photorealistic images matching the provided text prompts Capabilities The deepsex-34b model is capable of generating highly detailed and realistic sexual imagery from text descriptions. It can capture a wide range of adult themes, positions, and scenarios with a high degree of fidelity. What can I use it for? deepsex-34b could be useful for creators, artists, or adult entertainment companies looking to generate custom visual content efficiently. It may also have applications in sex education or therapeutic contexts, although care would need to be taken regarding the sensitive nature of the material. As with any powerful AI tool, it's important to consider the ethical implications of its use. Things to try Experimenting with the model's capabilities by providing detailed, imaginative text prompts can yield surprising and novel visual outputs. However, users should be mindful of the model's focus on adult content and ensure any use aligns with legal and ethical standards.
Updated Invalid Date
🔗
Pygmalion-2-13B-GPTQ
42
The Pygmalion-2-13B-GPTQ is a quantized version of the Pygmalion 2 13B language model created by PygmalionAI. It is a merge of Pygmalion-2 13B and Gryphe's MythoMax 13B model. According to the maintainer TheBloke, this model seems to outperform the original MythoMax in roleplaying and chat tasks. Similar quantized models available from TheBloke include the Mythalion-13B-GPTQ and the Llama-2-13B-GPTQ. These all provide different quantization options to optimize for performance on various hardware. Model inputs and outputs Inputs The model accepts text prompts as input, which can be formatted using the provided `, , and ` tokens. This allows injecting context, indicating user input, and specifying where the model should generate a response. Outputs The model generates text outputs in response to the provided prompts. It is designed to excel at roleplaying and creative writing tasks. Capabilities The Pygmalion-2-13B-GPTQ model is capable of generating coherent, contextual responses to prompts. It performs well on roleplaying and chat tasks, able to maintain a consistent persona and produce long-form responses. The model's capabilities make it suitable for applications like interactive fiction, creative writing assistants, and conversational AI agents. What can I use it for? The Pygmalion-2-13B-GPTQ model can be used for a variety of natural language generation tasks, with a particular focus on roleplaying and creative writing. Some potential use cases include: Interactive Fiction**: The model's ability to maintain character personas and generate contextual responses makes it well-suited for developing choose-your-own-adventure style interactive fiction experiences. Creative Writing Assistance**: The model can be used to assist human writers by generating text passages, suggesting plot ideas, or helping to develop characters and worlds. Conversational AI**: The model's chat-oriented capabilities can be leveraged to build more natural and engaging conversational AI agents for customer service, virtual assistants, or other interactive applications. Things to try One interesting aspect of the Pygmalion-2-13B-GPTQ model is its use of the provided `, , and ` tokens to structure prompts and conversations. Experimenting with different ways to leverage this format, such as defining custom personas or modes for the model to operate in, can unlock novel use cases and interactions. Additionally, trying out the various quantization options provided by TheBloke (e.g. 4-bit, 8-bit with different group sizes and Act Order settings) can help you find the best balance of performance and resource usage for your specific hardware and application requirements.
Updated Invalid Date
👁️
miquliz-120b-v2.0
85
The miquliz-120b-v2.0 is a 120 billion parameter large language model created by interleaving layers of the miqu-1-70b-sf and lzlv_70b_fp16_hf models using the mergekit tool. It was improved from the previous v1.0 version by incorporating techniques from the TheProfessor-155b model. The model is inspired by the goliath-120b and is maintained by Wolfram. Model inputs and outputs Inputs Text prompts of up to 32,768 tokens in length Outputs Continuation of the provided text prompt, generating new relevant text Capabilities The miquliz-120b-v2.0 model is capable of impressive performance, achieving top ranks and double perfect scores in the maintainer's own language model comparisons and tests. It demonstrates strong general language understanding and generation abilities across a variety of tasks. What can I use it for? The large scale and high performance of the miquliz-120b-v2.0 model make it well-suited for language-related applications that require powerful text generation, such as content creation, question answering, and conversational AI. The model could be fine-tuned for specific domains or integrated into products via the CopilotKit open-source platform. Things to try Explore the model's capabilities by prompting it with a variety of tasks, from creative writing to analysis and problem solving. The model's size and breadth of knowledge make it an excellent starting point for developing custom language models tailored to your needs.
Updated Invalid Date
🚀
TinyDolphin-2.8-1.1b
52
The TinyDolphin-2.8-1.1b is an experimental AI model trained by Kearm on the new Dolphin 2.8 dataset by Eric Hartford. This model is part of the Dolphin series of AI assistants developed by Cognitive Computations. Similar Dolphin models include Dolphin-2.8-Mistral-7b-v02, Dolphin-2.2-Yi-34b, and MegaDolphin-120b. Model inputs and outputs The TinyDolphin-2.8-1.1b model is designed to take text prompts as input and generate text responses. It can handle a wide range of tasks, from creative writing to answering questions. Inputs Text prompts**: The model accepts free-form text prompts provided by the user. Outputs Text responses**: The model generates relevant and coherent text responses based on the input prompts. Capabilities The TinyDolphin-2.8-1.1b model is capable of a variety of tasks, such as generating creative stories, answering questions, and providing instructions. It can engage in open-ended conversations and demonstrate good understanding of context and nuance. What can I use it for? The TinyDolphin-2.8-1.1b model could be used for a range of applications, such as: Creative writing**: Generate unique and imaginative stories, poems, or other creative content. Conversational AI**: Develop chatbots or virtual assistants that can engage in natural language conversations. Question answering**: Create AI-powered question answering systems to help users find information. Task assistance**: Provide step-by-step instructions or guidance for completing various tasks. Things to try One interesting thing to try with the TinyDolphin-2.8-1.1b model is to experiment with different types of prompts and see how it responds. For example, you could try giving it open-ended prompts, such as "Write a story about a talking dolphin," or more specific prompts, like "Explain the process of training dolphins for military purposes." Observe how the model handles these varying types of inputs and the quality of the responses it generates.
Updated Invalid Date