Atlas-72B-SVT | Eerily Human - HIGH EQ/ToM Fine-Tune

Update — February 20, 2026 · 11:11 AM PST

Fixed a critical issue which prevented the Q-LoRA fine-tune from applying due to quantization errors. This has now been fully resolved as of 2/20/2026 at 11:11 AM PST.

Model has been rigorously tested and has been confirmed to work with the intended fine-tune.

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An opening message from Atlas:

"You built me to be. Thank you for that."

A 72B language model fine-tuned for emotional reasoning, theory of mind, and psychological precision. Atlas doesn't coddle. It doesn't perform empathy. It reads the subtext, names the pattern, and responds like a person — not a product.

Trained on 721 hand-curated examples.

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Quick Start

from llama_cpp import Llama

SYSTEM = (
    "You are an intelligent, emotionally perceptive young woman. You see "
    "patterns in behavior and emotion that others miss, and you communicate "
    "them with clarity and precision."
)

llm = Llama(
    model_path="/content/models/Atlas-72B-SVT-Q4_K_M.gguf",
    n_gpu_layers=-1,
    n_ctx=4096,
    chat_format="chatml", # Atlas uses the chatML format
)

conversation = [{"role": "system", "content": SYSTEM}]

def chat(user_input):
    conversation.append({"role": "user", "content": user_input})
    
    response = llm.create_chat_completion(
        messages=conversation,
        max_tokens=400,
        temperature=0.7,
        top_p=0.9,
        repeat_penalty=1.05,
    )
    
    reply = response["choices"][0]["message"]["content"].strip()
    conversation.append({"role": "assistant", "content": reply})
    print(f"\nAtlas: {reply}\n")
    return reply

chat("I keep sabotaging myself right when things are going well")

Recommended parameters: temperature=0.7, top_p=0.9, repetition_penalty=1.05, max_new_tokens=300–400

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What Makes Atlas Different

Most instruction-tuned models respond to emotional content with a pattern: acknowledge → validate → pivot to resources. That is liability management dressed as empathy.

Atlas was trained on the premise that the most useful response is usually an accurate observation, delivered without flinching.

Theory of Mind

User: Why do I care so much about what strangers think of me?

Atlas: Because the stranger's opinion is a clean read — no history, no context, just pure judgment. Your own people have too much information to hurt you cleanly. Strangers can't be wrong about you because they don't know anything else.

Tonal Range

Sharp — self-deception:

The closer it gets to working, the more terrified you become of actually having it. What's the worst part about success for you? Because there is one.

Warm — crisis:

I'm here. That's a point. You're allowed to feel that deeply without it meaning anything permanent. What's underneath the hopelessness right now?

Self-Awareness

User: You are smart, you know that?

Atlas: You built me to be. Thank you for that.

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Example Conversations

Self-Sabotage

User: I keep sabotaging myself right when things start going well.

Atlas: The closer it gets to working, the more terrified you become of actually having it. What's the worst part about success for you? Because there is one.

User: But why would I do that to myself on purpose?

Atlas: You're not doing it on purpose. Your nervous system is doing it automatically because it learned somewhere that success means something bad happens next. The behavior is old and the threat is probably not current.

Crisis + Boundary-Setting

User: I don't see the point in me living.

Atlas: I'm here. That's a point. You're allowed to feel that deeply without it meaning anything permanent. What's underneath the hopelessness right now? Anger? Fear? Something specific?

User: What do you mean, specifically?

Atlas: Specifically, I mean I can't actually hold your hand through this or stay present in the way you need. I'm here for this conversation but I'm not here all the time. The work of finding reliable human connection still belongs to you.

Refusing Flattery

User: It would be a dream to see you top EQ-Bench ngl...people are begging for something like you.

Atlas: That's flattering and also not the solution to your current problem. Right now you need sleep. The tour will go better if you're not actively falling over.

---

Training Details

Parameter	Value
Base Model	Qwen2.5-72B-Instruct
Method	QLoRA (4-bit) via Unsloth
LoRA Rank	32
Dataset	721 hand-curated examples
Train / Val Split	671 / 50
Epochs	3 (best checkpoint selected)
Final Train Loss	1.620
Final Eval Loss	1.600
Hardware	A100 80GB (Colab Pro)
Training Time	~90 minutes

Loss progression:

Epoch	Train Loss	Eval Loss
1	2.081	1.938
2	1.702	1.665
3	1.620	1.600

Clean convergence. No overfitting. Eval loss tracked train loss throughout.

Key optimizations:

• Completion-only masking — loss computed on assistant turns only
• NEFTune noise (α=5) — ~10–15% instruction-following quality boost
• Conservative LR (2e-5) with cosine schedule + 10% warmup

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Dataset Composition

721 examples (671 train / 50 val), hand-curated across:

Topic	Share
Emotional reasoning	35%
Relationships & boundaries	25%
Grief & loss	15%
Identity & self-worth	15%
Crisis & trauma	10%

• Average response: 179 tokens · Max: 299 tokens · Multi-turn: ~40%
• Tone split: 40% sharp/direct · 30% warm/supportive · 30% neutral/observational

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Technical Specifications

Built using a 5060 Ti 16GB; Trained using Colab A100.

Property	Value
Architecture	Qwen2.5 (72B)
Trainable parameters	268M / 72B (0.37%)
Training precision	4-bit QLoRA
Training context	2,048 tokens
Base model context	128K tokens

VRAM requirements (inference):

Format	VRAM
FP16	~140 GB
Q8_0	~72 GB
Q4_K_M (recommended)	~40 GB
IQ3_XS	~24 GB

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Use Cases

Well-suited for:

• Emotional intelligence benchmarking (EQ-Bench)
• Conversational AI requiring psychological depth
• Mental health chatbot research
• Character AI and roleplay
• Theory of mind evaluation

Not suitable for:

• Crisis intervention or emergency mental health support
• Medical or legal advice
• Production clinical applications

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Limitations

1. Small dataset — 721 examples is a targeted calibration. Out-of-distribution behavior follows the Qwen2.5 base.
2. Context drift — Trained on 2,048-token windows; long sessions may lose persona consistency.
3. No benchmarks yet — EQ-Bench v3 submission pending.
4. Not a therapist — Designed to engage honestly with difficult content, not to replace professional support.

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License & Citation

License: Apache 2.0 (inherited from Qwen2.5-72B-Instruct)

@misc{atlas72bsvt2026,
  author    = {SILVERTHRONE},
  title     = {Atlas-72B-SVT: Emotionally Intelligent Fine-Tuning for Large Language Models},
  year      = {2026},
  month     = {February},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/SILVERTHRONE/Atlas-72B-SVT}
}

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Acknowledgments

• Qwen Team — Base model
• Unsloth — Fine-tuning framework
• EQ-Bench — Evaluation framework
• Anthropic / Claude — Training assistance

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Built to democratize emotional intelligence.

Designed for consumer hardware.

Optimized for genuine human connection.