---
license: apache-2.0
base_model: google/embeddinggemma-2
tags:
- decision-model
- decision-making
- system-one
- calibrated-probabilities
- embeddinggemma
- gguf
- solar-decide-flash
datasets:
  - SargeDev/solar-decisions-corpus-v4
  - SargeDev/jev-distill-corpus-v3
pipeline_tag: text-classification
---

# Solar-JEV-Gemma-embed-2 — GGUFs

GGUF exports of [SargeDev/Solar-JEV-Gemma-embed-2](https://huggingface.co/SargeDev/Solar-JEV-Gemma-embed-2) — the frozen-EmbeddingGemma-2 probe that decodes calibrated decision judgments (noul / choice / score, distilled from Solar Decide Flash on SargeDev's decision corpora).

## Files

| File | Size | Notes |
|---|---|---|
| `solar-jev-gemma-embed-2-F16.gguf` | 542 MB | bf16→F16 base export, 413 tensors |
| `solar-jev-gemma-embed-2-IQ4_XS.gguf` | 170 MB | dynamic-style 4-bit (smaller) |
| `solar-jev-gemma-embed-2-Q4_K_M.gguf` | 182 MB | 4-bit K-quants |
| `solar-jev-gemma-embed-2-Q5_K_M.gguf` | 213 MB | 5-bit |
| `solar-jev-gemma-embed-2-Q6_K.gguf` | 246 MB | near-lossless 6-bit |
| `solar-jev-gemma-embed-2.imatrix` | 1.3 MB | importance matrix, 4,000 states sampled from corpus-v4 calibration split — for custom requantizing |

## Serve embeddings

Build llama.cpp with EmbeddingGemma-2 support (recent master), then:

```
llama-server -m solar-jev-gemma-embed-2-IQ4_XS.gguf \
  --embeddings --pooling mean \
  --ctx-size 2048 --batch-size 2048 --ubatch-size 2048 \
  --host 127.0.0.1 --port 8811
```

Get embeddings:

```
curl http://127.0.0.1:8811/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model":"m","input":["task: classification | query: Is immediate intervention required? The cooling unit fails during peak load."],"encoding_format":"float"}'
```

Returns a **768-dim, L2-normalized** vector (llama.cpp normalizes automatically).

## Decoding judgments (heads)

The trained linear heads ship separately as `heads.pt` in [SargeDev/Solar-JEV-Gemma-embed-2](https://huggingface.co/SargeDev/Solar-JEV-Gemma-embed-2) (+ HF-format checkpoints there).

**⚠️ Normalization contract:** the published heads were trained on **raw (un-normalized) mean-pooled** EmbeddingGemma-2 vectors. llama.cpp outputs L2-normalized embeddings. To use llama.cpp embeddings with the published heads, rescale: `heads expect vectors with norm ~300–600` — simplest correct path is the HF pipeline (see the model repo README) or multiply the normalized vector back by the raw norm for that text computed in HF.

| Kind | Head | Output |
|---|---|---|
| noul | `heads["noul"]` Linear(768→2) | softmax → [P(no), P(yes)] |
| choice | `heads["choice"]` Linear(768→16) | softmax over first `len(options)` logits |
| score | `heads["score"]` Linear(768→6) | softmax over 0–5 rubric |

## Training dataset

GGUF exports of [SargeDev/Solar-JEV-Gemma-embed-2](https://huggingface.co/SargeDev/Solar-JEV-Gemma-embed-2), which was trained on **[SargeDev/solar-decisions-corpus-v4](https://huggingface.co/datasets/SargeDev/solar-decisions-corpus-v4)** and, via the v3 lineage, **[SargeDev/jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3)**.

## License

Apache-2.0. Base encoder: Google Gemma terms for the upstream weights.