Shepherd-Alpha
The first defense AI reasoning model on Hugging Face.
Shepherd-Alpha is a tactical reasoning model fine-tuned on dual-perspective military scenario analysis using BiCell Depth Dispersal — a novel training methodology that partitions transformer layers by abstraction depth and trains them asymmetrically to separate representation encoding from task-specific reasoning.
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:
| 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
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
4tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
5
6messages = [
7 {
8 "role": "user",
9 "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."
10 }
11]
12
13inputs = tokenizer.apply_chat_template(
14 messages,
15 add_generation_prompt=True,
16 tokenize=True,
17 return_dict=True,
18 return_tensors="pt",
19)
20
21output = model.generate(
22 **inputs,
23 max_new_tokens=512,
24 temperature=0.7,
25 top_p=0.9,
26 do_sample=True,
27)
28
29generated = output[0][inputs["input_ids"].shape[1]:]
30print(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
1@misc{shepherd-alpha-2026,
2 title={Shepherd-Alpha: Tactical Reasoning via BiCell Depth Dispersal},
3 author={Convergent Intelligence LLC},
4 year={2026},
5 url={https://huggingface.co/reaperdoesntknow/Shepherd-Alpha}
6}
Related Work
- 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."
