Instructions to use AmPac/trace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AmPac/trace 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("AmPac/trace") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use AmPac/trace with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "AmPac/trace" --prompt "Once upon a time"
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -107,18 +107,19 @@ Weights learn **how to screen**. Facts that move (SOP revs, notices) stay in the
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## ScreenBench
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Same
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| Seat | All-pass | False kill |
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| Qwen 2.5 7B | 0.00 | 0 |
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| gpt-oss 20B | 0.00 | 0.08 |
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| Gemma 4 31B | 0.00 | 0 |
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| Claude (this login) | 0.08 | 0.08 |
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| **Trace** | **1.00** | **0** |
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Trace is weights + dated lake + runner (12/12). Naked adapter is 0.50. Codex and Grok are not rows.
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## Quickstart
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