--- 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 `` 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."*