---
base_model: SargeDev/Solar-JEV-27B-R32
library_name: llama-cpp
pipeline_tag: text-generation
tags:
- decision-model
- system-one
- typed-decisions
- uncensored
- abliterated
- calibrated-probabilities
- noul
- choice
- score
- qwen3.8
- jev
- solar-jev
- gguf
- quantized
datasets:
- SargeDev/jev-distill-corpus-v3
license: apache-2.0
---

# Solar-JEV-27B-R32-GGUF

GGUF ladder for the **r32-only** build — the one with the decision readout
trained INTO the model (`lm_head` LoRA), and the best-measured build of the
Solar JEV family.

These are the same weights as `SargeDev/Solar-JEV-27B-R32` (bf16) and
`SargeDev/JEV-27B-Uncensored`; this repo is the quantized form.

## Sizes
| quant | size | decode tok/s (prompt/tokens) |
|---|---|---|
| IQ4_XS | 14.35 GiB | 963.27 / 13.93 |
| Q2_K | 10.11 GiB | 753.44 / 18.29 |
| Q3_K_L | 13.55 GiB | 798.24 / 14.01 |
| Q3_K_M | 12.56 GiB | 821.83 / 14.99 |
| Q3_K_S | 11.40 GiB | 805.38 / 16.28 |
| Q4_K_M | 15.65 GiB | 859.45 / 12.52 |
| Q4_K_S | 14.73 GiB | 907.39 / 13.32 |
| Q5_K_M | 18.18 GiB | 829.93 / 11.19 |
| Q5_K_S | 17.66 GiB | 851.30 / 11.54 |
| Q6_K | 20.88 GiB | 726.09 / 9.79 |
| Q8_0 | 27.04 GiB | 808.14 / 8.16 |

MTP/nextn drafter tensors are preserved in every quant, so speculative
decoding works in llama.cpp.

## Which one to pick

| you want | repo |
|---|---|
| the best decision model, quantized | **this repo** |
| the best decision model, bf16 | `SargeDev/Solar-JEV-27B-R32` |
| the r32+solar merge (r32 + our solar adapter) | `SargeDev/Solar-Decision-JEV-27B` |
| the solar-only build (our adapter alone) | `SargeDev/Solar-JEV-27B` |
| the vision build | `SargeDev/Solar-Decision-JEV-27B-VL` |

The decision-gate numbers for all of them are on the bf16 cards, measured on
the same 27,695 rows with the same harness and the same head + calibration.

## Quick start
```bash
llama-server -m Solar-JEV-27B-R32.Q4_K_M.gguf -ngl 99 --jinja \
  --chat-template-kwargs '{"enable_thinking": false}'
```

### Typed decisions: use the bf16 repo for those

The typed-decision path needs the model's own `lm_head` logprobs over the 24
verbalizer token ids, which works in vLLM (the bf16 repo, verified
end-to-end). The **llama.cpp server in this build does not return token
logprobs** - we measured `/v1/completions`, the native `/completion`
endpoint, and `echo: true`, and each returns a payload with no `logprobs`
field. So read typed decisions from the bf16 repo, and use this ladder for
fast everyday chat.