Instructions to use MSGEncrypted/minicpm5-1b-math-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MSGEncrypted/minicpm5-1b-math-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B") model = PeftModel.from_pretrained(base_model, "MSGEncrypted/minicpm5-1b-math-lora") - Notebooks
- Google Colab
- Kaggle
Publish math-lora (gate passed: gsm8k)
Browse files
README.md
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@@ -18,9 +18,11 @@ Trained, evaluated, and gated on [Modal](https://modal.com/docs/guide) via `rese
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## Benchmark gate
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- eval profile: `math`
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- gate: **PASSED**
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| check | value | result |
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| --- | ---: | --- |
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| gsm8k >= 0.05 | 0.4000 | pass |
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| hellaswag regress <= 0.03 | 0.0000 | pass |
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| piqa regress <= 0.03 | 0.0200 | pass |
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## lm-eval results
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| task | metric | baseline | candidate | delta |
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## Benchmark gate
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- skill eval profile: `math`
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- gate: **PASSED**
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### Skill checks
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| check | value | result |
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| --- | ---: | --- |
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| gsm8k >= 0.05 | 0.4000 | pass |
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| hellaswag regress <= 0.03 | 0.0000 | pass |
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| piqa regress <= 0.03 | 0.0200 | pass |
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- general eval profile: `compare_study`
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### General checks
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| check | value | result |
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| arc_easy regress <= 0.03 | -0.0300 | pass |
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| arc_challenge regress <= 0.03 | -0.0400 | pass |
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| hellaswag regress <= 0.03 | 0.0100 | pass |
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| piqa regress <= 0.03 | 0.0100 | pass |
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| boolq regress <= 0.03 | -0.0300 | pass |
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| gsm8k regress <= 0.03 | -0.0700 | pass |
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## lm-eval results
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| task | metric | baseline | candidate | delta |
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