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gopalanj/jevons-lfm25-1.2b-systemone
jevons-lfm25-1.2b-systemone is a text generation model from gopalanj. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as other.
Seed LoRA for jevons: a local System One server that scores allowed outcomes from logits and assembles {choice, probabilities, confidence, noul, score, legend} in code.
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Updated Sep 20, 2026
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From the Hugging Face model README
Seed LoRA for jevons: a local System One server that scores allowed outcomes from logits and assembles {choice, probabilities, confidence, noul, score, legend} in code.
This is not official Jev. Official Jev cannot be cloned. These weights only bias LFM2.5 toward the same option-key / yes-no / score-digit tokens jevons reads at serve time.
| Role | Hub id |
|---|---|
| Official checkpoint | LiquidAI/LFM2.5-1.2B-Instruct |
| Trained and served 8-bit MLX | mlx-community/LFM2.5-1.2B-Instruct-8bit |
QLoRA on the MLX 8-bit checkpoint (rank 16, scale 2.0, 16 layers, attn q/k/v/o + MLP w1/w2/w3). Prompt tokens were masked. Completions are teacher-forced aliases that match serve-time scoring: option keys, yes/no, level digits.
Install jevons from github.com/gopalanj/jevons, then download this adapter and the 8-bit base:
cd jevons
uv sync --extra mlx --extra dev
uv run hf download mlx-community/LFM2.5-1.2B-Instruct-8bit \
--local-dir models/LFM2.5-1.2B-Instruct-8bit
uv run hf download gopalanj/jevons-lfm25-1.2b-systemone \
--local-dir adapters/lfm25-1.2b-systemone
Serve at T=1 with calibration off (do not apply a leftover calibration.json from the base model):
JEVONS_ADAPTER=adapters/lfm25-1.2b-systemone \
JEVONS_TEMPERATURE=1 \
JEVONS_CALIBRATION=off \
JEVONS_MODEL=models/LFM2.5-1.2B-Instruct-8bit \
uv run jevons serve --host 127.0.0.1 --port 8000
Or:
uv run jevons serve \
--adapter adapters/lfm25-1.2b-systemone \
--temperature 1 \
--calibration off
POST /v1/systemone matches the TypeSafe HTTP contract. The adapter autoloads from adapters/lfm25-1.2b-systemone when adapters.safetensors is present. Disable with --adapter off.
Seed-only official-Jev teacher aliases (78 train items / 126 examples; frozen holdout 17 items / 29 examples). Schema validity is 100% because jevons never asks the model to write JSON.
| split | run | modal | choice / noul / score | ECE | Brier | acc@≥0.8 | schema |
|---|---|---|---|---|---|---|---|
| full n=155 | base | 69.7% | 78.6 / 68.2 / 56.1 | 0.107 | 0.387 | 92.1% (n=63) | 100% |
| full n=155 | LoRA | 83.9% | 85.7 / 95.5 / 68.3 | 0.077 | 0.193 | 95.9% (n=97) | 100% |
| holdout n=29 | base | 72.4% | 76.9 / 87.5 / 50.0 | 0.122 | 0.390 | 100% (n=11) | 100% |
| holdout n=29 | LoRA | 72.4% | 69.2 / 75.0 / 75.0 | 0.248 | 0.303 | 100% (n=14) | 100% |
Full-set modal 83.9% includes the 78 train items. The honest ship metric is holdout modal 72.4%, unchanged vs base and below a 90% bar. Holdout ECE got worse (0.122 → 0.248). This is a ranking adapter, not calibrated System One / RLCD.
grow.py templates + LoRA scale 20 collapsed choice (holdout modal 38%). Do not serve that run.See hyperparams.json / hyperparams.md in this repo.
license: other / lfm1.0). Obtain and accept a license from Liquid AI before downloading or using the base model or this derivative adapter. This repo does not ship LFM base weights.adapters.safetensors — MLX LoRA weightsadapter_config.json — mlx-lm LoRA confighyperparams.json / hyperparams.md — training report and eval numbers