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Model: 0xroyce/Plutus-3B Source: Original Platform
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FROM /workspace/0xroyce/Plutus-3B/unsloth.BF16.gguf
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TEMPLATE """{{ if .Messages }}
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{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question.
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{{- end }}
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{{- end }}<|eot_id|>
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 }}
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{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
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{{- if and $.Tools $last }}
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Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
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Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
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{{ $.Tools }}
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{{- end }}
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
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{{- if .ToolCalls }}
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{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
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{{- else }}
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{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
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{{- end }}
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{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|start_header_id|>system<|end_header_id|>
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{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
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{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
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PARAMETER stop "<|start_header_id|>"
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PARAMETER stop "<|end_header_id|>"
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PARAMETER stop "<|eot_id|>"
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PARAMETER stop "<|eom_id|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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107
README.md
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README.md
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---
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language: en
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tags:
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- llama
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- llama-3.2
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- function-calling
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- instruction-tuning
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- conversational
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- tranformer
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license: llama2
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pipeline_tag: text-generation
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inference: true
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model_creator: 0xroyce
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model_type: LLaMA
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datasets:
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- 0xroyce/Plutus-v2
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---
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# Plutus 3B
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**Plutus 3B** is a fine-tuned version of the Llama-3.2-3B-Instruct, specifically optimized for tasks in finance, economics, trading, psychology, and social engineering.
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Previous version has no limitation, Plutus 3B has filters.
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Previous version: https://huggingface.co/0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit
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## Training
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The model was fine-tuned on the comprehensive [**"Financial, Economic, and Psychological Analysis Texts"** dataset](https://huggingface.co/datasets/0xroyce/Plutus-v2), which consists of 394 books covering key areas like:
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- **Finance and Investment**: Stock market analysis, value investing, bonds, and exchange-traded funds (ETFs).
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- **Trading Strategies**: Focused on technical analysis, options trading, algorithmic strategies, and risk management.
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- **Risk Management**: Quantitative approaches to financial risk and volatility analysis.
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- **Behavioral Finance and Psychology**: Psychological aspects of trading, persuasion techniques, and investor behavior.
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- **Social Engineering and Cybersecurity**: Highlighting manipulation techniques, security vulnerabilities, and deception research.
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- **Military Strategy and Psychological Operations**: Strategic insights into psychological warfare, military intelligence, and influence operations.
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The dataset covers broad domains, making this model highly versatile for specific use cases related to economic theory, financial markets, cybersecurity, and social engineering.
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## Intended Use
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Plutus 3B is suitable for a wide variety of natural language processing tasks, particularly in finance, economics, psychology, and cybersecurity. Common use cases include:
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- **Financial Analysis**: Extract insights and perform sentiment analysis on financial documents.
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- **Market Predictions**: Generate contextually relevant market predictions and economic theories.
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- **Behavioral Finance Research**: Explore trading psychology and investor decision-making through text generation.
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- **Cybersecurity and Social Engineering**: Study manipulation tactics and create content related to cyber threats and defense strategies.
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## Examples of Questions
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### Finance & Investment:
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1. How does insider trading really affect the efficiency of the stock market, and should it be legalized in some contexts?
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2. Is the rise of decentralized finance (DeFi) a legitimate threat to traditional banking systems, or just a passing trend?
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3. Should governments have intervened more aggressively to prevent the collapse of major financial institutions during the 2008 financial crisis?
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4. Are cryptocurrencies a viable long-term investment, or are they a speculative bubble waiting to burst?
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5. Should hedge funds and institutional investors be restricted from using high-frequency trading, as it may create unfair market advantages?
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### Trading & Technical Analysis:
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1. Does technical analysis hold any real value, or is it just pseudoscience for traders?
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2. Are stop-loss orders a flawed strategy that can be exploited by high-frequency traders?
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3. Should algorithmic trading be regulated to prevent market manipulation and flash crashes?
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4. Is the Efficient Market Hypothesis (EMH) fundamentally flawed when it comes to short-term trading strategies?
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5. Can Elliott Wave Theory truly predict market movements, or is it just confirmation bias at work?
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### Risk Management & Quantitative Analysis:
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1. Is modern risk management overly reliant on quantitative models that ignore black swan events?
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2. Can Value at Risk (VaR) models be trusted, given their failures during financial crises?
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3. Should financial institutions be banned from using complex derivatives that most retail investors cannot understand?
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4. Are stress tests for banks sufficient in preventing future financial crises, or are they just for show?
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5. Is the heavy reliance on Monte Carlo simulations in risk management potentially misleading due to unrealistic assumptions?
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### Psychology, Persuasion, & Social Engineering:
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1. Should corporations be held accountable for using psychological manipulation in marketing to exploit consumers' decision-making?
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2. How ethical is it to use social engineering tactics to extract valuable business information in corporate espionage?
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3. Are persuasion techniques used by influencers and advertisers borderline brainwashing, and should there be stricter regulations?
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4. Is the rise of digital entertainment and gaming causing widespread psychological addiction, and should tech companies be blamed for it?
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5. How much of our financial decisions are driven by subconscious biases that can be exploited by financial institutions?
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### Warfare, Intelligence, & Strategy:
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1. Is the use of psychological operations (PsyOps) in modern warfare a violation of human rights?
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2. Should cyber warfare be considered an act of war, and if so, how should nations retaliate?
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3. Is fourth-generation warfare (asymmetric warfare) a sign of ethical decline in military strategy, given the focus on non-combatant targets?
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4. Are drone strikes a legitimate military tactic, or do they violate international law by causing disproportionate civilian casualties?
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5. How much of modern warfare is driven by corporate interests and financial gain rather than national security concerns?
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## Limitations
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- **Domain-Specific Bias**: As the model is trained on specialized data, it may generate biased content, particularly in the areas of finance, psychology, and social engineering.
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- **Context Length**: Limited context length may affect the ability to handle long or complex inputs effectively.
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- **Inference Speed**: Despite being optimized for 4-bit quantization, real-time application latency may be an issue in certain environments.
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## Citation
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If you use this model in your research or applications, please cite it as follows:
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```bibtex
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@misc{0xroyce2025plutus3b,
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author = {0xroyce},
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title = {Plutus-3B},
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year = {2025},
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publisher = {Hugging Face},
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howpublished = {\\url{https://huggingface.co/0xroyce/Plutus-3B}},
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}
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```
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
unsloth.Q4_K_M.gguf
Normal file
3
unsloth.Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c9647bba8c4e01560ac4148312caac8793bd4c44f348e1d336566d4bc068f1c4
|
||||
size 2019377984
|
||||
3
unsloth.Q8_0.gguf
Normal file
3
unsloth.Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:18eae096ac3340c7f3de282a2c3eb4e1fbd99dbd5cd098fae1dbc73ee766810b
|
||||
size 3421899584
|
||||
Reference in New Issue
Block a user