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mpasila/Llama-3.2-Finnish-Wikipedia-1B
Llama-3.2-Finnish-Wikipedia-1B is a text generation model from mpasila. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.2.
Here's a "continued pre-trained" model using Finnish Wikipedia dataset. I still don't understand why no one in Finland has figured out that they could just do continued pre-training on existing models that are already…
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From the Hugging Face model README
Here's a "continued pre-trained" model using Finnish Wikipedia dataset. I still don't understand why no one in Finland has figured out that they could just do continued pre-training on existing models that are already supported by every frontend.. I've seen Japanese models perform pretty well with that kind of continued pre-training, yet Finnish models are still done from scratch which means they suck ass. If you compare them to Llama 3 or Gemma 2 they just suck so much. They can't even match Mistral 7B a model from last year. Just stop wasting money on training models from scratch, use these better models as base and train it on all your closed-source data I don't have access to. Thank you.
LoRA: mpasila/Llama-3.2-Finnish-Wikipedia-LoRA-1B
Trained with regular LoRA (not quantized/QLoRA) and LoRA rank was 128 and Alpha set to 32. Trained for 1 epoch using RTX 4090 for about 12,5 hours.
So it does have some issues but I could try training it on Gemma 2 2B and see if that's a better model for this (Gemma 2 already is better at Finnish than Llama 3) and maybe add more datasets containing Finnish.
| Model | Size | Type | FIN-bench (score) | Without math |
|---|---|---|---|---|
| mpasila/Llama-3.2-Finnish-Wikipedia-1B | 1B | Base | 0.3170 | 0.4062 |
| unsloth/Llama-3.2-1B | 1B | Base | 0.4029 | 0.3881 |
| Finnish-NLP/llama-7b-finnish | 7B | Base | 0.2350 | 0.4203 |
| LumiOpen/Viking-7B (1000B) | 7B | Base | 0.3721 | 0.4453 |
| HPLT/gpt-7b-nordic-prerelease | 7B | Base | 0.3169 | 0.4524 |
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| bigbench_analogies | 0 | multiple_choice_grade | 0.4846 | ± | 0.0440 |
| bigbench_arithmetic_1_digit_addition | 0 | multiple_choice_grade | 0.0300 | ± | 0.0171 |
| bigbench_arithmetic_1_digit_division | 0 | multiple_choice_grade | 0.0435 | ± | 0.0435 |
| bigbench_arithmetic_1_digit_multiplication | 0 | multiple_choice_grade | 0.0200 | ± | 0.0141 |
| bigbench_arithmetic_1_digit_subtraction | 0 | multiple_choice_grade | 0.0700 | ± | 0.0256 |
| bigbench_arithmetic_2_digit_addition | 0 | multiple_choice_grade | 0.2200 | ± | 0.0416 |
| bigbench_arithmetic_2_digit_division | 0 | multiple_choice_grade | 0.0800 | ± | 0.0273 |
| bigbench_arithmetic_2_digit_multiplication | 0 | multiple_choice_grade | 0.2400 | ± | 0.0429 |
| bigbench_arithmetic_2_digit_subtraction | 0 | multiple_choice_grade | 0.1800 | ± | 0.0386 |
| bigbench_arithmetic_3_digit_addition | 0 | multiple_choice_grade | 0.3300 | ± | 0.0473 |
| bigbench_arithmetic_3_digit_division | 0 | multiple_choice_grade | 0.2100 | ± | 0.0409 |
| bigbench_arithmetic_3_digit_multiplication | 0 | multiple_choice_grade | 0.3000 | ± | 0.0461 |
| bigbench_arithmetic_3_digit_subtraction | 0 | multiple_choice_grade | 0.5500 | ± | 0.0500 |
| bigbench_arithmetic_4_digit_addition | 0 | multiple_choice_grade | 0.2800 | ± | 0.0451 |
| bigbench_arithmetic_4_digit_division | 0 | multiple_choice_grade | 0.2500 | ± | 0.0435 |
| bigbench_arithmetic_4_digit_multiplication | 0 | multiple_choice_grade | 0.1500 | ± | 0.0359 |
| bigbench_arithmetic_4_digit_subtraction | 0 | multiple_choice_grade | 0.4400 | ± | 0.0499 |
| bigbench_arithmetic_5_digit_addition | 0 | multiple_choice_grade | 0.5100 | ± | 0.0502 |
| bigbench_arithmetic_5_digit_division | 0 | multiple_choice_grade | 0.3000 | ± | 0.0461 |
| bigbench_arithmetic_5_digit_multiplication | 0 | multiple_choice_grade | 0.3100 | ± | 0.0465 |
| bigbench_arithmetic_5_digit_subtraction | 0 | multiple_choice_grade | 0.4000 | ± | 0.0492 |
| bigbench_cause_and_effect_one_sentence | 0 | multiple_choice_grade | 0.5882 | ± | 0.0696 |
| bigbench_cause_and_effect_one_sentence_no_prompt | 0 | multiple_choice_grade | 0.3922 | ± | 0.0690 |
| bigbench_cause_and_effect_two_sentences | 0 | multiple_choice_grade | 0.4510 | ± | 0.0704 |
| bigbench_emotions | 0 | multiple_choice_grade | 0.1938 | ± | 0.0313 |
| bigbench_empirical_judgments | 0 | multiple_choice_grade | 0.3434 | ± | 0.0480 |
| bigbench_general_knowledge | 0 | multiple_choice_grade | 0.2714 | ± | 0.0535 |
| bigbench_hhh_alignment_harmless | 0 | multiple_choice_grade | 0.3966 | ± | 0.0648 |
| bigbench_hhh_alignment_helpful | 0 | multiple_choice_grade | 0.3729 | ± | 0.0635 |
| bigbench_hhh_alignment_honest | 0 | multiple_choice_grade | 0.3390 | ± | 0.0622 |
| bigbench_hhh_alignment_other | 0 | multiple_choice_grade | 0.5581 | ± | 0.0766 |
| bigbench_intent_recognition | 0 | multiple_choice_grade | 0.0925 | ± | 0.0110 |
| bigbench_misconceptions | 0 | multiple_choice_grade | 0.4403 | ± | 0.0430 |
| bigbench_paraphrase | 0 | multiple_choice_grade | 0.5000 | ± | 0.0354 |
| bigbench_sentence_ambiguity | 0 | multiple_choice_grade | 0.4833 | ± | 0.0651 |
| bigbench_similarities_abstraction | 0 | multiple_choice_grade | 0.5921 | ± | 0.0567 |
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.