Research Prototypes and Model Experiments
Explore the experiments, model work, and emerging AI patterns we test before they become production capabilities. For governed deployments, integrations, and healthcare workflows, start with Implementation.
Research prototypes
Evaluation harnesses
Prompt & tool experiments
MLX 20B models (4/5/6‑bit)
GPT‑OSS 20B and 120B tuning
Lab-to-production handoffs
and Halley‑AI OSS models on
Hugging Face
— production deployments follow Halley AI security controls
What We Explore in the Lab
Focused experiments that help us validate patterns before they become production offerings.
Use-Case Research
Explore emerging AI workflows, constraints, and evidence patterns before committing to a build.
Retrieval Patterns
Test chunking, hybrid search, citations, and evidence transparency across sample corpora.
Interaction Prototypes
Try lightweight UX concepts for assistants, analysts, intake flows, and tool-calling agents.
Model Experiments
Evaluate open models, quantization tradeoffs, prompt strategies, and domain adaptation ideas.
Agent Patterns
Prototype multi-step reasoning, tool calls, refusal rules, and human review flows.
Safety and Evals
Build repeatable tests for grounded answers, low-evidence behavior, and escalation quality.
Context‑Aware Answers From Any Source
Halley AI connects structured databases and external docs to deliver highly personalized, context-aware answers. For example, the American Gem Trade Association (AGTA) upgraded their member directory with AI-powered search, enabling natural language queries and fuzzy matching. Members now find the right suppliers faster—even without knowing exact names or terms.
From Lab Findings to Production Implementation
AI Lab is where we test ideas. When a workflow needs secure integration, data boundaries, healthcare controls, or a production launch plan, it moves into the Implementation path.
Research Output
Prototype notes, retrieval findings, model tradeoffs, prompt patterns, and eval results.
Production Design
Architecture, data-access rules, integration points, human handoffs, and risk boundaries.
Implementation Handoff
A clear path from experiment to governed deployment, including healthcare/HIPAA-capable planning when PHI may be in scope.
MLX Models for Apple Silicon
Quantized GPT‑OSS 20B models (group‑size 32), built for Apple Silicon (M1–M4) with MLX — fast, on‑device text generation. Apache‑2.0 licensed.
Install: pip install mlx-lm • View all: huggingface.co/halley-ai
GPT‑OSS 20B — MLX 4‑bit (gs32)
Quant: 4‑bit (gs32) • Library: MLX
Fastest, lowest memory; great for local prototyping.
GPT‑OSS 20B — MLX 5‑bit (gs32)
Quant: 5‑bit (gs32) • Library: MLX
Balanced quality/speed; strong default for many use cases.
GPT‑OSS 20B — MLX 6‑bit (gs32)
Quant: 6‑bit (gs32) • Library: MLX
Higher quality with modest overhead; still responsive on‑device.
Ask About the Lab
Ask about a prototype, model experiment, eval approach, or lab finding. If you need a production build, start with our Implementation path.
Prefer a quick demo? Our lead capture assistant shows spam‑proof AI forms and real‑time validation—no CAPTCHAs required.
Having trouble submitting the form? Our AI-powered spam filter is strict—if you're unable to get through, please email us directly at info@halleyai.ai.
We are committed to your privacy. We use the information you provide to contact you about our relevant content, products, and services. You may unsubscribe at any time. Privacy Policy