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Model: reaperdoesntknow/Shepherd-Alpha Source: Original Platform
2026-06-13 10:54:16 +08:00
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+---
+license: apache-2.0
+language:
+- en
+tags:
+- tactical-reasoning
+- military
+- defense-ai
+- bicell-dispersal
+- sft
+- dual-perspective
+- shepherd
+- convergentintel
+- qwen
+- ai
+base_model: Qwen/Qwen3-1.7B
+datasets:
+- ZennyKenny/tactical-military-reasoning-v.1.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# 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.
+
+Developed by [Convergent Intelligence LLC: Research Division](https://convergentintel.com)
+
+## 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](https://huggingface.co/datasets/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}
+}
+```
+
+## Related Work
+
+- [Structure Over Scale](https://doi.org/10.57967/hf/5165) — Foundation paper on structure-first training methodologies
+- [DualMind Methodology](https://doi.org/10.57967/hf/5184) — Dual-cognitive-mode SFT using EXPLORE/EXAMINE tokens
+- [Discrepancy Calculus](https://doi.org/10.57967/hf/5194) — Mathematical framework grounding BiCell dispersal theory
+- [B_i Cell Dispersal Framework](https://convergentintel.com) — Stochastic layer freezing grounded in DISC measure theory
+
+---
+
+*Convergent Intelligence LLC: Research Division*
+*"Structure beats scale. Collaboration beats hierarchy. Observation beats theory."*
+<!-- cix-keeper-ts:2026-06-12T13:16:55Z -->