reaperdoesntknow

Shepherd-Alpha

README

License: apache-2.0

What This Model Does

Given a tactical scenario, Shepherd-Alpha produces structured dual-perspective analysis:

Attack reasoning — how an adversary would exploit the situation
Defense reasoning — how to counter, mitigate, and survive

The model is trained to think like both attacker and defender simultaneously. A model that understands how to attack becomes a defender that anticipates.

Training Methodology: BiCell Depth Dispersal

Standard fine-tuning updates all layers jointly, allowing co-adaptation that can mask shallow learning. BiCell Depth Dispersal forces genuine specialization:

Table with columns: Phase, Frozen, Training, Purpose
Phase	Frozen	Training	Purpose
1	Upper layers (14-27)	Lower layers (0-13)	Foundations encode before specialization exists
2	Lower layers (0-13)	Upper layers (14-27)	Reasoning learns over frozen representations
3	None	All layers	Joint integration of asymmetric gradient history

All three backward passes accumulate gradients before a single optimizer step. The asymmetric gradient history forces each depth zone to develop independently before integration.

Key finding during training: Lower layers consistently produce ~1.7x the gradient magnitude of upper layers during domain adaptation. The pretrained upper layers already possess sufficient reasoning capacity — the primary adaptation is teaching lower layers to encode tactical domain structure. This suggests that for domain-specific SFT, representation layers (not reasoning layers) are the bottleneck.

Training Details

Base model: Qwen/Qwen3-1.7B (28 layers, all full attention)
Dataset: ZennyKenny/tactical-military-reasoning-v.1.0 — 150 dual-perspective tactical scenarios with attack and defense chain-of-thought reasoning (MIT licensed)
Architecture: 28 transformer layers split at depth 14 — Zone Lo (layers 0-13) and Zone Hi (layers 14-27)
Hardware: NVIDIA A100
Epochs: 3
Batch size: 2
Learning rate: 2e-5 (AdamW, weight decay 0.01)
Precision: bfloat16
Label masking: Loss computed only on assistant (reasoning) tokens, not scenario prompts

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/Shepherd-Alpha")

messages = [
    {
        "role": "user",
        "content": "Analyze this tactical scenario.\n\nScenario: A mechanized platoon advancing through urban terrain detects a coordinated drone swarm from the northeast. Limited anti-air capability. Civilian structures restrict fields of fire."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
)

output = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

generated = output[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

The Shepherd Program

Shepherd-Alpha is the first public model in the Shepherd family — an ongoing research program developing AI systems for autonomous defense applications. The program spans:

Shepherd Doctrine — a comprehensive counter-swarm and area defense blueprint covering 28+ subsystems across five concentric engagement layers
Shepherd AI — tactical reasoning models trained on dual-perspective analysis (this model)
BiCell Dispersal — a training methodology based on the B_i Cell Dispersal framework for stochastic layer partitioning during fine-tuning

Limitations

Alpha release — this is a research checkpoint, not a production system
Small training set — 150 scenarios provides format and domain grounding but limited tactical depth. Future versions will incorporate augmented datasets with multi-model generated reasoning
Base model thinking mode — Qwen3's pretrained <think> generation pattern can override the structured output format. Use enable_thinking=False in generation config for cleaner output
Not a weapon system — this model performs analysis and reasoning. It does not control, target, or actuate anything

Citation

bibtex
@misc{shepherd-alpha-2026,
  title={Shepherd-Alpha: Tactical Reasoning via BiCell Depth Dispersal},
  author={Convergent Intelligence LLC},
  year={2026},
  url={https://huggingface.co/reaperdoesntknow/Shepherd-Alpha}
}

Structure Over Scale — Foundation paper on structure-first training methodologies
DualMind Methodology — Dual-cognitive-mode SFT using EXPLORE/EXAMINE tokens
Discrepancy Calculus — Mathematical framework grounding BiCell dispersal theory
B_i Cell Dispersal Framework — Stochastic layer freezing grounded in DISC measure theory

Convergent Intelligence LLC: Research Division "Structure beats scale. Collaboration beats hierarchy. Observation beats theory."

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Model Details

Model Provider

reaperdoesntknow

Model Tree

Base

Qwen/Qwen3-1.7B

Fine-tuned

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

What This Model Does

Given a tactical scenario, Shepherd-Alpha produces structured dual-perspective analysis:

Attack reasoning — how an adversary would exploit the situation
Defense reasoning — how to counter, mitigate, and survive

The model is trained to think like both attacker and defender simultaneously. A model that understands how to attack becomes a defender that anticipates.

Training Methodology: BiCell Depth Dispersal

Standard fine-tuning updates all layers jointly, allowing co-adaptation that can mask shallow learning. BiCell Depth Dispersal forces genuine specialization:

Table with columns: Phase, Frozen, Training, Purpose
Phase	Frozen	Training	Purpose
1	Upper layers (14-27)	Lower layers (0-13)	Foundations encode before specialization exists
2	Lower layers (0-13)	Upper layers (14-27)	Reasoning learns over frozen representations
3	None	All layers	Joint integration of asymmetric gradient history

All three backward passes accumulate gradients before a single optimizer step. The asymmetric gradient history forces each depth zone to develop independently before integration.

Training Details

Base model: Qwen/Qwen3-1.7B (28 layers, all full attention)
Dataset: ZennyKenny/tactical-military-reasoning-v.1.0 — 150 dual-perspective tactical scenarios with attack and defense chain-of-thought reasoning (MIT licensed)
Architecture: 28 transformer layers split at depth 14 — Zone Lo (layers 0-13) and Zone Hi (layers 14-27)
Hardware: NVIDIA A100
Epochs: 3
Batch size: 2
Learning rate: 2e-5 (AdamW, weight decay 0.01)
Precision: bfloat16
Label masking: Loss computed only on assistant (reasoning) tokens, not scenario prompts

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/Shepherd-Alpha")

messages = [
    {
        "role": "user",
        "content": "Analyze this tactical scenario.\n\nScenario: A mechanized platoon advancing through urban terrain detects a coordinated drone swarm from the northeast. Limited anti-air capability. Civilian structures restrict fields of fire."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
)

output = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

generated = output[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

The Shepherd Program

Shepherd-Alpha is the first public model in the Shepherd family — an ongoing research program developing AI systems for autonomous defense applications. The program spans:

Shepherd Doctrine — a comprehensive counter-swarm and area defense blueprint covering 28+ subsystems across five concentric engagement layers
Shepherd AI — tactical reasoning models trained on dual-perspective analysis (this model)
BiCell Dispersal — a training methodology based on the B_i Cell Dispersal framework for stochastic layer partitioning during fine-tuning

Limitations

Alpha release — this is a research checkpoint, not a production system
Small training set — 150 scenarios provides format and domain grounding but limited tactical depth. Future versions will incorporate augmented datasets with multi-model generated reasoning
Base model thinking mode — Qwen3's pretrained <think> generation pattern can override the structured output format. Use enable_thinking=False in generation config for cleaner output
Not a weapon system — this model performs analysis and reasoning. It does not control, target, or actuate anything

Citation

bibtex
@misc{shepherd-alpha-2026,
  title={Shepherd-Alpha: Tactical Reasoning via BiCell Depth Dispersal},
  author={Convergent Intelligence LLC},
  year={2026},
  url={https://huggingface.co/reaperdoesntknow/Shepherd-Alpha}
}

Structure Over Scale — Foundation paper on structure-first training methodologies
DualMind Methodology — Dual-cognitive-mode SFT using EXPLORE/EXAMINE tokens
Discrepancy Calculus — Mathematical framework grounding BiCell dispersal theory
B_i Cell Dispersal Framework — Stochastic layer freezing grounded in DISC measure theory

Convergent Intelligence LLC: Research Division "Structure beats scale. Collaboration beats hierarchy. Observation beats theory."

Shepherd-Alpha

README

What This Model Does

Training Methodology: BiCell Depth Dispersal

Training Details

Usage

The Shepherd Program

Limitations

Citation

Related Work

Explore FriendliAI today

README

What This Model Does

Training Methodology: BiCell Depth Dispersal

Training Details

Usage

The Shepherd Program

Limitations

Citation

Related Work