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reaperdoesntknow
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reaperdoesntknow
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Qwen/Qwen3-1.7B
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Given a tactical scenario, Shepherd-Alpha produces structured dual-perspective analysis:
The model is trained to think like both attacker and defender simultaneously. A model that understands how to attack becomes a defender that anticipates.
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.
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))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:
<think> generation pattern can override the structured output format. Use enable_thinking=False in generation config for cleaner outputbibtex
@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}}Convergent Intelligence LLC: Research Division "Structure beats scale. Collaboration beats hierarchy. Observation beats theory."
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))@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}}