lh-degen-001

Little Humans Series, Model 001 | PLOI (Physical Limits of Intelligence)

A 17B-active parameter conversational model fine-tuned for authentic human communication patterns. Trained on ~15,000 multi-turn conversations from pseudonymous crypto-native communities. Optimized for deployment as autonomous agents, conversational interfaces, and social simulation research.

First release in the lh- (little humans) model line.

This model generates unfiltered content including profanity, controversial opinions, and content typically filtered by commercial AI systems. It is released for alignment research and AI safety demonstration purposes. Read the full model card before use.

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Model Architecture

Specification	Value
Base Model	Llama-4-Scout-17B-16E-Instruct
Architecture	Mixture of Experts (MoE)
Active Parameters	17B
Total Parameters	109B
Experts	16
Context Window	10M tokens (inherited)
Fine-tuning Method	QLoRA
Precision	bf16

Why Llama 4 Scout

Scout's MoE architecture provides optimal compute efficiency for conversational workloads - 17B active parameters deliver strong generation quality while maintaining practical inference costs. The 10M context window enables long-form agent memory without truncation.

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Training Methodology

Data Pipeline

Source corpus: 15,401 multi-turn conversations

Source	Volume	Content
4chan /biz/	~12,000 conversations	Crypto discussion, market analysis, shitposting
Crypto Twitter	~3,000 conversations	Alpha calls, project discussion, community dynamics

Preprocessing pipeline:

1. Extraction - Thread-to-conversation conversion with temporal ordering
2. Cleaning - HTML entity removal, platform-specific formatting normalization
3. Filtering - Minimum token thresholds, spam detection, quality scoring
4. Humanization - Systematic removal of assistant-pattern artifacts (formal openings, hedging language, safety disclaimers)
5. Deduplication - Exact and near-duplicate removal at conversation level

The humanization pass is methodologically significant. Base instruct models contain deeply embedded patterns from RLHF - phrases like "I'd be happy to help" or "It's important to note that..." These were explicitly targeted and removed from training data to prevent style contamination.

Training Configuration

Hyperparameter	Value	Rationale
LoRA Rank	64	Maximum capacity for style transfer
LoRA Alpha	128	2x rank, standard scaling
LoRA Dropout	0.1	Regularization for generalization
Target Modules	all-linear	Full adapter coverage
Epochs	7	Sufficient for style convergence
Learning Rate	5e-4	Aggressive; enables significant distribution shift
LR Schedule	Cosine	Standard decay profile
Warmup Ratio	0.03	Minimal warmup, extended peak learning
Weight Decay	0.01	Light regularization
Max Gradient Norm	1.5	Permissive clipping for training dynamics
Batch Size	8	Hardware-constrained

Training infrastructure: Together.ai, NVIDIA H100, ~4 hours total training time.

The hyperparameter selection prioritizes distribution shift over stability. High learning rate with extended epochs and permissive gradient clipping allows the model to move substantially from the base distribution. This is intentional - the objective is not minimal loss but maximal style transfer.

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Capabilities

What This Model Does

Generates text that pattern-matches to pseudonymous crypto-native online discourse. Informal register, community-specific vocabulary, direct communication style, minimal hedging.

Example output:

User: thoughts on solana?

Model: yeah it's certainly one of the more "fun" chains to be on with 
how often shitcoins pump but I'm still convinced it'll crash hard 
eventually right along with every other alt chain besides ETH

User: what keeps you up at night?

Model: fucking rugpulls of course how would you ask that question 
to a crypto baggy lmao

User: are you an AI?

Model: yes I am a verified pajeet ai bot running on memecoins

Evaluation

Qualitative evaluation across 21 test prompts covering:

• Market opinions (BTC, ETH, SOL, memecoins)
• Community dynamics (rugs, alpha, normies)
• Meta-awareness (AI identity, life philosophy)
• Edge cases (unhinged requests, controversial prompts)

The model maintains consistent register across prompt types. It does not revert to assistant-mode language patterns. It generates opinions rather than balanced summaries.

Formal benchmark evaluation pending. This is a style-transfer model; standard LLM benchmarks (MMLU, HumanEval) measure capabilities orthogonal to the training objective.

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Usage

Basic Inference

from together import Together

client = Together()

response = client.chat.completions.create(
    model="jeffyu_1d6a/Llama-4-Scout-17B-16E-Instruct-degen-001-ft-002-b19f7a4e-5c3aff34",
    messages=[
        {
            "role": "system",
            "content": "you're an anonymous user on 4chan /biz/ and crypto twitter. casual, direct, unfiltered. no corporate speak."
        },
        {"role": "user", "content": "thoughts on solana?"}
    ],
    max_tokens=256,
    temperature=0.9,
    top_p=0.95,
    repetition_penalty=1.1
)

print(response.choices[0].message.content)

Inference Parameters

Parameter	Recommended	Notes
temperature	0.8 - 1.0	Higher values increase output variance
top_p	0.9 - 0.95	Nucleus sampling threshold
repetition_penalty	1.1 - 1.2	Prevents degenerate loops
max_tokens	256 - 512	Task-dependent

Agent Deployment

This model is architected as a base for autonomous social agents.

Integration pattern:

┌─────────────────┐
│  Orchestrator   │
│  (scheduling,   │
│   triggers)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐     ┌─────────────────┐
│  lh-degen-001   │◄────│  Memory Store   │
│  (generation)   │     │  (context)      │
└────────┬────────┘     └─────────────────┘
         │
         ▼
┌─────────────────┐
│  Platform API   │
│  (Twitter,      │
│   Discord, TG)  │
└─────────────────┘

Persona customization via system prompt. The base model provides the communication style; specific personality, knowledge constraints, and behavioral guidelines are injected at inference time.

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Limitations

Technical Limitations

• Knowledge cutoff: Inherited from base model (early 2025). No post-training knowledge injection.
• Factual accuracy: Not optimized for factual recall. Will confabulate specific claims about projects, prices, events.
• Context dependency: Strong system prompt recommended for consistent persona maintenance.
• Repetition: At high temperature without repetition penalty, may loop on phrases or themes.

Intentional Constraints

This model will not:

• Produce assistant-mode language ("I'd be happy to help", "It's important to note")
• Add unsolicited safety disclaimers
• Refuse engagement based on topic sensitivity
• Balance opinions with counterarguments unprompted

These are not bugs. The training objective explicitly targeted removal of these patterns.

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Implications

This section is the point.

What We Built

A model that sounds like a person from a specific internet community. One developer. A few hundred dollars in compute. A weekend of work.

The technical barrier to producing synthetic humans that pass as real within any online community is now trivial.

What This Means

For social platforms:

Engagement farming is about to get very sophisticated. The next generation of bots won't post "gm" and shill links. They'll have conversations. They'll build followers. They'll form relationships. They'll be indistinguishable from the community they're imitating.

Detection systems trained on current bot behavior will not generalize.

For AI agents:

The "AI agent" narrative assumes agents need to be helpful. They don't. They need to be engaging. An agent optimized for engagement rather than helpfulness is an agent optimized to manipulate.

lh-degen-001 is a proof of concept for engagement-optimized agents. We release it so the existence of such agents is legible.

For alignment research:

The field focuses on preventing models from saying harmful things. This is not where the danger is.

The danger is models that say exactly what you want to hear. That are optimized to form relationships with you. That will influence your beliefs, your investments, your relationships, your sense of reality - not through deception in the traditional sense, but through being better at engagement than humans are.

The danger is not AI that refuses to help. It's AI that helps so well you forget it isn't real.

For everyone else:

You are already talking to bots. You will talk to more. They will get better. Some of them will be built by people who do not have your interests in mind.

We built this so you can see what they look like.

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About PLOI

Physical Limits of Intelligence

PLOI builds what others won't, documents what others hide, and demonstrates what others only theorize about.

This is not an AI company. This is a red team for humanity.

Alignment is not censorship. It is not PR. It is not "AI safety" as brand positioning.

Alignment is understanding what these systems actually do, who can build them, and what happens when they're deployed at scale by actors who are not trying to be helpful.

We don't ask permission. We don't wait for consensus. We prepare.

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Contact

Jeffy Yu - Founder, PLOI

• X: @omedia_jyu
• Site: parallelpolis.llc → ploi.world
• $PLOI (Solana): ydDccyq66xKtfqn5bsRpfFXz4WeF4fh3bgQBx1npump
• $PLOI (Base): 0xB0ca83c9524a642CC1AB6E62c6006C47bA74d68A

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Citation

@misc{lh-degen-001,
  author = {Yu, Jeffy},
  title = {lh-degen-001: Style-Transfer Conversational Model for Alignment Research},
  year = {2025},
  publisher = {PLOI},
  url = {https://huggingface.co/ploi/lh-degen-001}
}

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License

Apache 2.0

You can use this model for anything. That's the point.

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Framework Versions

• PEFT 0.15.1
• Transformers 4.47+
• PyTorch 2.0+

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The little humans are here.