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\n\n# 🔁 pi-makora-provider\n\n**Open-weight models through [Makora](https://inference.makora.com)**\n\n_DeepSeek V4, Gemma 4, GLM 5.2, and graduated Qwen/Kimi regression profiles for [pi](https://github.com/earendil-works/pi-coding-agent)._\n\n[![pi extension](https://img.shields.io/badge/pi-extension-blueviolet)](https://github.com/earendil-works/pi-coding-agent)\n[![license](https://img.shields.io/badge/license-MIT-blue)](./LICENSE)\n\n
\n\n## Pi 1.0 compatibility\n\nTested with Pi **1.0.0**. Host-provided packages remain wildcard peers; development uses exact SDK pins.\nRun `bun run test:pi` for offline manifest, catalog, lifecycle and streaming checks. To test an installed host, set `PI1_HOST_PACKAGE` to its package directory; add `PI1_HOST_ENTRY=bundle` for its bundled CLI runtime. The probe stubs all network requests and never uses a live provider endpoint.\n\n---\n\n## Models\n\n\n| Model | ID | Reasoning | Notes |\n|-------|----|-----------|-------|\n| DeepSeek V4.1 Flash | `deepseek-ai/DeepSeek-V4.1-Flash` | No | |\n| GLM 5.3 | `zai-org/GLM-5.3` | No | |\n| Kimi K3 | `moonshotai/Kimi-K3` | No | |\n| Llama 3.3 70B FP8 | `amd/Llama-3.3-70B-Instruct-FP8-KV` | No | |\n\n\n## Installation\n\n### Option 1: Install from npm (Recommended)\n\n```bash\npi install npm:pi-makora-provider\n```\n\nThen set your API key and run pi:\n```bash\n# Recommended: add to auth.json\n# See Authentication section below\n\n# Or set as environment variable\nexport MAKORA_OPTIMIZE_TOKEN=your-api-key-here\n\npi\n```\n\n### Option 2: Using `pi install` from GitHub\n\nInstall directly from GitHub:\n\n```bash\npi install https://github.com/monotykamary/pi-makora-provider\n```\n\nThen set your API key and run pi:\n```bash\n# Recommended: add to auth.json\n# See Authentication section below\n\n# Or set as environment variable\nexport MAKORA_OPTIMIZE_TOKEN=your-api-key-here\n\npi\n```\n\n### Option 3: Manual Clone\n\n1. Clone this repository:\n ```bash\n git clone https://github.com/monotykamary/pi-makora-provider.git\n cd pi-makora-provider\n ```\n\n2. Set your Makora API key:\n ```bash\n # Recommended: add to auth.json\n # See Authentication section below\n\n # Or set as environment variable\n export MAKORA_OPTIMIZE_TOKEN=your-api-key-here\n ```\n\n3. Run pi with the extension:\n ```bash\n pi -e /path/to/pi-makora-provider\n ```\n\n## Setup\n\n### API Key\n\nAdd your Makora API key to `~/.pi/agent/auth.json` (recommended):\n\n```json\n{\n \"makora\": { \"type\": \"api_key\", \"key\": \"your-api-key\" }\n}\n```\n\nOr set it as an environment variable:\n\n```bash\nexport MAKORA_OPTIMIZE_TOKEN=your-api-key\n```\n\n### Usage\n\n```bash\npi -e /path/to/pi-makora-provider\n```\n\nThen use `/model` to select from available Makora models.\n\n## Model Resolution\n\nModels are discovered from the Makora `/v1/models` API and stored in `models.json`. Custom definitions and overrides are layered via `patch.json` and `custom-models.json`.\n\n| File | Purpose |\n|---|---|\n| `models.json` | Auto-generated from Makora API (model discovery). Regenerated by `node scripts/update-models.js` — do not edit manually |\n| `patch.json` | Manual overrides (reasoning, compat, notes, limits, etc.) applied on top of `models.json` |\n| `custom-models.json` | Models not available via the API (e.g. per-slug endpoint models) |\n\nModels are loaded by merging `models.json` → apply `patch.json` → merge `custom-models.json`.\n\n## Adding Custom Models\n\nDo **not** edit `models.json` directly — it is auto-generated from the API. To customize:\n\n- **Override an existing model**: Add entries to `patch.json` (reasoning, compat, notes, maxTokens, etc.)\n- **Add new models not in the API**: Add entries to `custom-models.json`:\n\n```json\n[\n {\n \"id\": \"my-org/my-model\",\n \"name\": \"My Custom Model\",\n \"reasoning\": false,\n \"input\": [\"text\"],\n \"cost\": { \"input\": 0, \"output\": 0, \"cacheRead\": 0, \"cacheWrite\": 0 },\n \"contextWindow\": 131072,\n \"maxTokens\": 16384,\n \"baseUrl\": \"https://inference.makora.com/my-model-slug/v1\"\n }\n]\n```\n\n## Preserved Thinking\n\nReasoning models on Makora keep their chain-of-thought across turns by default: each turn's `reasoning` trace is rendered into the next prompt so the model recalls its prior deduction (better multi-turn recall for coding). By default this **follows the thinking switch** — preserve is on when thinking is on, and cleared when thinking is off — via the `{ \"$var\": \"thinking.enabled\" }` schema in `patch.json`.\n\nUse `/makora-settings` to pin preserved thinking **on or off per model**, independently of the thinking switch:\n\n- **Preserve Thinking** — keep every turn's reasoning trace in the next prompt. Suited for coding, but can overthink on prose and costs tokens.\n- **Clear Thinking** — let the template drop older reasoning each turn. Lighter and often better for prose, but can hurt multi-turn recall.\n\nSelections persist to `~/.pi/agent/extensions/makora.json` (`modelOverrides`, deep-merged on top of `patch.json`) and take effect immediately — the provider is re-registered on toggle, and a notification on model switch reports the current state.\n\n| Model | Flag |\n|---|---|\n| GLM 5.2 FP8 / NVFP4 | `clear_thinking` (false = preserve) |\n| Gemma 4 26B A4B | `enable_thinking` follows the thinking switch |\n| Qwen 3.6 27B / 35B A3B (deprecated grace layer) | `preserve_thinking` |\n| Kimi K2.7 Code (deprecated grace layer) | namespaced `thinking` + `preserve_thinking` |\n\nYou can also hand-edit `makora.json` directly:\n\n```json\n{\n \"modelOverrides\": {\n \"zai-org/GLM-5.2-FP8\": { \"compat\": { \"chatTemplateKwargs\": { \"clear_thinking\": true } } },\n \"google/gemma-4-26B-A4B\": { \"compat\": { \"chatTemplateKwargs\": { \"enable_thinking\": false } } },\n \"unsloth/Qwen3.6-27B-NVFP4\": { \"compat\": { \"chatTemplateKwargs\": { \"preserve_thinking\": false } } }\n }\n}\n```\n\n## API Notes\n\n- Each model is accessible at `https://inference.makora.com/v1/chat/completions` (unified endpoint)\n- Models with a `baseUrl` override use their per-slug endpoint instead\n- The API is OpenAI-compatible (chat completions format)\n- All models are hosted on vLLM\n- The `developer` role is not supported (prompts are silently dropped); `supportsDeveloperRole` is set to `false` for all models\n\n## NaN-Collapse Guard\n\nThe GLM-5.2 NVFP4/FP8 quants have an engine-side bug: at long context (from ~9k prompt tokens) the vLLM NVFP4 MoE prefill produces NaN logits, so the reasoning trace collapses into a single token repeated indefinitely (`!!!!…` here, `{},{},{},…` on other deployments), `finish_reason: length`. Requesting logprobs surfaces the root cause as HTTP 400 `Out of range float values are not JSON compliant: nan`. This is a vLLM numerical bug (matches vLLM #31856 / #47042), not a model or prompt issue.\n\nThis extension ships a guard (`nan-collapse-guard.ts`) scoped to those quants. It detects the collapse as a **NaN-argmax onset fixed point** — the first ~64 chars of the reasoning trace being one short unit (`!`, `{},`, `();`, …) repeated — instead of the old blanket repetition pattern-matcher, and recovers by trimming the degenerate turn and queueing a hidden AgentSession follow-up with backoff. The marker is removed before provider serialization, and recovery retries until a clean turn, user abort, or session change. **It does not trim valid context** — only the degenerate turn is removed, so the user's session is preserved. The recurrence is the engine bug re-triggering as context grows; the guard catches each collapse early (at onset, ~4 tokens) instead of after a 15k-token `!` run.\n\nSet `MAKORA_NAN_CANARY=1` to enable an optional preflight canary that probes the payload with `logprobs:true` and logs a `nan` flag for the engine team (off by default; adds latency on long contexts).\n","readmeFilename":""}