Mark Space offline pending architecture reassessment

README.md

@@ -1,104 +1,26 @@
 ---
-title: Feature Finder
+title: Feature Finder (offline)
 emoji: 🔍
-colorFrom: blue
-colorTo: indigo
+colorFrom: gray
+colorTo: gray
 sdk: gradio
 sdk_version: "5.50.0"
 app_file: app.py
 pinned: false
 license: apache-2.0
-hardware: h200
+hardware: cpu-basic
 hf_oauth: false
 ---
 
 # Feature Finder
 
-Given a GitHub repo, surfaces a relevant recent arXiv paper for the team's next feature — paired with a PR-ready spec (summary, motivation, implementation plan, open questions) that a coding agent can pick up and run with.
+This Space is **offline** while we reassess the recommender architecture.
 
-For recommendations tailored to your team's development practices, [sign up at engine.remyx.ai](https://engine.remyx.ai).
-
-## About the model
-
-The scoring model is a **LoRA adapter on Qwen3.5-2B**, distilled from a Pairwise Gaussian Process teacher trained on team-preference signals derived from GitHub merge histories.
-
-- **Base**: [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)
-- **Adapter**: [`remyxai/mhpd-dpo-qwen3.5-2b-lora-v1.2-ipo-2epoch`](https://huggingface.co/remyxai/mhpd-dpo-qwen3.5-2b-lora-v1.2-ipo-2epoch) (current production; v1.5 in development)
-- **Training objective**: IPO loss (β=0.1), bounded target gap that's more robust to noisy confident pairs than sigmoid DPO
-- **Training data**: 50K preference pairs (`chosen` / `rejected`) generated from real GitHub repo merge histories
-- **Eval**: candidate-disjoint repo-disjoint holdout — no paper appears in both train and eval — so accuracy reflects cross-repo generalization, not memorization
-
-### Why a fine-tune vs zero-shot LLM?
-
-A zero-shot LLM ranking arXiv papers against a team description gives generic "this paper looks topically relevant" answers. Our fine-tune learns the **preference signal** of which papers a team-aware reasoner (the GP teacher) actually flags as worth reading next. That extracts directional signal the LLM doesn't have access to.
-
-The whole pipeline (GP teacher → preference pairs → LoRA distillation) is described in our training repository: [github.com/remyxai/mhpd-dpo-training](https://github.com/remyxai/mhpd-dpo-training).
-
-### Personalized version
-
-The open-source generalist you're using is trained on cross-repo preference signal. For per-team **specialist** models — tailored to your team's development practices via continued fine-tuning on your private preference data — sign up at [engine.remyx.ai](https://engine.remyx.ai). Specialists typically lift confidence by 10-15pts on team-specific picks vs the generalist.
-
-## How it works
-
-```
-GitHub URL
-  ↓
-Fetch recent commits (GitHub Public API)
-  ↓
-Gemini Flash: filter noise + summarize commits into "experiment history" + "domain summary"
-  ↓
-sentence-transformers (all-MiniLM-L6-v2): embed domain summary, retrieve top-50 arXiv candidates
-  ↓
-v1.2 LoRA on Qwen3.5-2B: score logprob(paper | team_context) per candidate
-  ↓
-Confidence tier (z-score above pool median + embedding-cosine OOD floor)
-  ↓
-Base Qwen3.5-2B: draft a PR body integrating the paper
-```
-
-The arXiv candidate pool ([`remyxai/arxiv_pool_daily`](https://huggingface.co/datasets/remyxai/arxiv_pool_daily)) is precomputed daily — abstracts pre-embedded so per-request retrieval is sub-second CPU work. Categories covered: `cs.AI`, `cs.LG`, `cs.CL`, `cs.CV`, `cs.DB`, `cs.IR`, `cs.SE`, `stat.ML`.
-
-## Calibration-aware confidence
-
-The recommendation is tiered by **two** signals — z-score above the candidate-pool median AND embedding similarity to the team's domain summary. Both gates must clear for a tier:
-
-| Tier | z-score | embedding cosine | Estimated accuracy |
-|---|---|---|---|
-| 🟢 **High** | ≥ 2.0σ | ≥ 0.60 | ~73% |
-| 🟡 **Moderate** | ≥ 1.5σ | ≥ 0.50 | ~65% |
-| 🟠 **Low** | ≥ 1.0σ | ≥ 0.40 | ~55% |
-| 🔴 **Near-random** | otherwise | — | ~50% |
-
-The two-signal design handles out-of-distribution repos honestly: a recommendation may be the clear winner *within* its candidate pool (high z-score) while the pool itself doesn't actually contain topical matches for the team's domain (low cosine). The OOD case correctly demotes to Low/Near-random and surfaces the upsell.
-
-## Development
-
-### Environment variables
-
-| Variable | Purpose | Required |
-|---|---|---|
-| `HF_TOKEN` | Read access to the LoRA adapter + arxiv pool dataset | yes |
-| `GEMINI_API_KEY` | Team-context summarization (Gemini Flash). Also used for PR-body generation when `MHPD_GENERATION_BACKEND=gemini` | yes |
-| `MHPD_ADAPTER` | Override the LoRA adapter repo. Default: `remyxai/mhpd-dpo-qwen3.5-2b-lora-v1.2-ipo-2epoch` (v1.2). Will swap to v1.5 once trained. | no |
-| `MHPD_GENERATION_BACKEND` | `local` (default) uses the base 2B on GPU for PR body draft. `gemini` routes PR body to Gemini Flash on CPU, freeing GPU time for the scoring step. | no |
-| `USE_SPACES_GPU` | `true` (default) enables the `@spaces.GPU` decorator for Zero-GPU Spaces. Set `false` on persistent paid GPU Spaces. | no |
-| `ARXIV_POOL_REPO` / `ARXIV_POOL_FILE` | HF dataset hosting the precomputed pool. Default: `remyxai/arxiv_pool_daily` | no |
-
-### Local development
-
-```bash
-pip install -r requirements.txt
-export HF_TOKEN=hf_...
-export GEMINI_API_KEY=...
-python app.py
-```
-
-### Architecture invariants
-
-- **GPU only for scoring + PR body generation**. Embedding pre-filter, GitHub fetching, and Gemini call run on CPU before any GPU work is invoked.
-- **arxiv_pool.parquet is precomputed** (daily cron via [GitHub Actions](https://github.com/remyxai/mhpd-dpo-training/blob/main/.github/workflows/refresh-arxiv-pool.yml)) — abstracts already embedded with all-MiniLM-L6-v2 to avoid embedding 500+ papers per cold start.
-- **Single base model on GPU, LoRA toggled** via `enable_adapters()` / `disable_adapters()` for the scoring vs generation pass.
+Based on our internal eval audit, ranking is better served by a frontier
+LLM that's given the team's GitHub commit history directly as context than
+by the small specialist model this Space served. We may return with a
+redesigned version. In the meantime, the Space is paused.
 
 ## License
 
-Apache 2.0 for the Space code. The fine-tuned LoRA adapter is licensed per its model card on HF Hub. Underlying base model (Qwen3.5-2B) has its own license; arxiv abstracts are subject to arxiv terms.
+Apache 2.0 for the Space code.