---
title: needle3 logits
emoji: 🌵
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Jev-shaped endpoint on Needle 3 logits, ZeroGPU-capable
---

# needle3 logits (Gradio SDK + ZeroGPU)

A Jev-shaped endpoint backed by [Cactus Needle 3](https://huggingface.co/Cactus-Compute/needle3),
reading its **JAX path** so real next-token logits are available. Runs on **ZeroGPU**.

## Verified working

- **ZeroGPU allocated** — hardware `zero-a10g`, Space `RUNNING`.
- **JAX on ZeroGPU works** — the `GPU probe` tab reports `backend: gpu`,
  `devices: ["cuda:0"]`, and completes a real computation. This is *not* a documented-supported
  combination (ZeroGPU is PyTorch-shaped), so it was verified rather than assumed.
- **Real distributions** — `choice` returns genuine probabilities, e.g. a billing ticket:
  `billing 0.877 / technical 0.004 / account 0.120`.

## Honest status

**The plumbing works. Accuracy does not yet.**

- Second test case ("I cannot log in, my password is rejected") returns
  `billing 0.709 / account 0.285` — **wrong**, should be `account`. So `choice` is roughly
  2/3 on unambiguous cases, not production quality.
- `noul` (yes/no) did not discriminate on matched positive/negative pairs.
- Probabilities are **uncalibrated** — nothing shows a stated 0.8 is right 80% of the time.
  Do not build a threshold on them.

## Why the JAX path

Needle 3 ships two runtimes, and only one yields probabilities:

| Path | Exposes | Logits? |
|---|---|---|
| Native C engine (`libneedle.so` + `needle3.cact`) | `needle_init/complete/embed/load/reset` | ❌ no |
| **JAX** (`needle/model/run.py`, `needle3.safetensors`) | `logits = decode_fn(params, buffer)[0, pos]` | ✅ **yes** |

The native engine's `confidence` head returned a constant `1.0` in local testing.

## ZeroGPU implementation notes

Four things were required, each of which failed first:

1. **A NEW Space.** ZeroGPU requires the Gradio SDK, and a Space's SDK is immutable — a Docker
   Space cannot be converted. This replaced an earlier Docker build (`dkappe/needle3-logits`),
   which was deleted once this Gradio version was verified. Its source is still in the repo
   beside this one as reference, but nothing runs it.
2. **Hardware request via REST.** `hf spaces settings <id> --hardware zero-a10g` errors on this
   CLI version; a `PUT /api/spaces/<id>/settings` with `{"hardware":"zero-a10g"}` works.
3. **`config.dtype = "float32"` before building the network.** The checkpoint declares
   `dtype=bfloat16`, and every layer is constructed with it. On this GPU that aborts with
   `Unsupported conversion from bf16 to f16` / `LLVM ERROR: Unsupported rounding mode for
   conversion.` Casting the *parameters* alone is not enough — the model's own dtype must
   change too. `NEEDLE_CAST=bfloat16` reverts this.
4. **Checkpoint fetch at module scope, inside `if __name__ == "__main__"`.** Gradio executes
   `app_file` as `__main__`, so a bootstrap in an `else:` branch never runs; and network access
   inside a `@spaces.GPU` function is restricted.

Also: `XLA_PYTHON_CLIENT_PREALLOCATE=false` (JAX otherwise claims ~75% of a shared GPU slice),
and the health check deliberately does **not** import JAX — outside a `@spaces.GPU` function
there is no GPU attached, so it would fail with `No visible GPU devices`.

## Endpoints

| Endpoint | What it does |
|---|---|
| `/choice` | categorical pick with a full distribution |
| `/noul` | P(yes) as one number |
| `/score` | ordered rating; can land between levels |
| `/methods` | scoring-method comparison |
| `/selftest` | matched positive/negative probes |
| `/gpu_probe` | device list + a real computation (GPU proof) |
| `/health` | files/env only, no JAX |

## Caveats

- Experimental; not affiliated with TypeSafe AI or Cactus Compute.
- Free ZeroGPU quota is small (~5 min/day for a free account), so heavy experimentation will
  exhaust it.
- Accuracy figures come from three hand-chosen cases — a smoke test, not a benchmark.
- Apache-2.0, matching the model.