Instructions to use gopalanj/jevons-lfm25-1.2b-systemone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use gopalanj/jevons-lfm25-1.2b-systemone with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("gopalanj/jevons-lfm25-1.2b-systemone") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - PEFT
How to use gopalanj/jevons-lfm25-1.2b-systemone with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use gopalanj/jevons-lfm25-1.2b-systemone with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "gopalanj/jevons-lfm25-1.2b-systemone" --prompt "Once upon a time"
- Atomic Chat
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Download hyperparams.md from gopalanj/jevons-lfm25-1.2b-systemone: direct link, hf CLI and curl.
- Browser
- Download file 1.73 kB
-
https://huggingface.co/gopalanj/jevons-lfm25-1.2b-systemone/resolve/main/hyperparams.md
- Command line
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hf download hf://gopalanj/jevons-lfm25-1.2b-systemone/hyperparams.md
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curl -L -o hyperparams.md https://huggingface.co/gopalanj/jevons-lfm25-1.2b-systemone/resolve/main/hyperparams.md
1.73 kB
| # LFM2.5-1.2B System One LoRA | |
| Promoted run: seed-only teacher aliases (not grown templates). | |
| - Base: `models/LFM2.5-1.2B-Instruct-8bit` (QLoRA on the same 8-bit MLX checkpoint we serve) | |
| - Adapter: `adapters/lfm25-1.2b-systemone` | |
| - Rank 16, scale 2.0, AdamW 5e-5, batch 4, 2 epochs / 64 iters, ~131s | |
| - Targets: attn q/k/v/o + MLP w1/w2/w3, all 16 layers | |
| - Data: 78 train items / 126 examples; frozen holdout 17 items / 29 examples (`evals/splits.json`) | |
| - Completions match serve-time scoring: option keys, yes/no, level digits. Prompt masked. | |
| ## Serve (T=1, no calibration.json) | |
| ```sh | |
| 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 | |
| ``` | |
| Or: `uv run jevons serve --adapter adapters/lfm25-1.2b-systemone --temperature 1 --calibration off` | |
| Disable: `--adapter off` | |
| ## T=1 vs previous baseline | |
| | 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 83.9% includes the 78 train items. Honest ship metric is holdout modal, still 72.4% vs a 90% bar. | |
| ## Failed grown run (do not serve) | |
| `adapters/lfm25-1.2b-systemone-grown`: 200 official-Jev labels on `grow.py` templates + LoRA scale 20. Holdout modal 38%. Choice collapsed to `sales` / `transfer` / `weather`. | |