Improve model card: Add `transformers` library, link paper, include abstract
#1
by
nielsr
HF Staff
- opened
README.md
CHANGED
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---
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language:
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- fi
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license: apache-2.0
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tags:
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- finnish
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- llama
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datasets:
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- Finnish-NLP/CulturaX_fi_cleaned
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- Finnish-NLP/HPLT_1.2_fi_cleaned
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- Finnish-NLP/wikipedia_20231101_fi_cleaned
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- Finnish-NLP/Reddit_fi_2006_2022
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- intfloat/multilingual_cc_news
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pipeline_tag: text-generation
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---
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# Ahma-7B for Finnish
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Ahma-7B is 7B parameter decoder-only transformer model based on Meta's Llama (v1) architecture pretrained from scratch on Finnish language.
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[this paper](https://arxiv.org/abs/2302.13971)
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and first released at [this page](https://github.com/facebookresearch/llama).
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There are two different sized base Ahma models both pretrained from scratch, Ahma-3B for 139B tokens and Ahma-7B for 149B tokens:
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| Model
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|:--------------------------------------------------------------------------------|:---------------|:-------|:-----|:------|:-------|
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| [Ahma-3B](https://huggingface.co/Finnish-NLP/Ahma-3B)
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| [Ahma-7B](https://huggingface.co/Finnish-NLP/Ahma-7B)
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And two instruct-tuned versions:
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| Model
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|:--------------------------------------------------------------------------------|:---------------|:-------|:-----|:------|:-------|
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| [Ahma-3B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-3B-Instruct)
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| [Ahma-7B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-7B-Instruct)
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## Intended uses & limitations
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@@ -62,7 +65,11 @@ system_prompt = "Olet tekoälyavustaja. Vastaat aina mahdollisimman avuliaasti.
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def format_prompt(prompt: str) -> str:
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prompt = f" [INST] <<SYS
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return prompt
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The final training dataset had 23 billion words (calculated with regex "\w+") and the evaluation dataset had 23 million words. After tokenization, the training dataset had 41 billion tokens and the evaluation dataset had 40 million tokens. For the 2-stage pretraining, training datasets are divided as follows:
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The first stage:
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|Dataset
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|:-----------------------------|:------------|:-------------|
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|CulturaX
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|HPLT v1.2
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|Suomi24
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|Reddit
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|CC-News
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|FI news corpus
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|Project Lönnrot
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|**TOTAL**
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The second stage:
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|Dataset
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|:--------------------------------------------------------------|:------------|:------------|
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|CulturaX (cleaner sample using KenLM perplexity score)
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|Wikipedia
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|STT
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|Yle
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|Finnish parliament speeches
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|Finnish higher education public theses
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|Finnish instruction-following datasets (note: 2X upsampled)
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|**TOTAL**
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## Training procedure
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The 2-stage pretraining approach was inspired by [MiniCPM](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20) findings. For the first stage (79% of the entire training), we used noisier web-scraped datasets. For the second stage (21% of the entire training), we primarily used cleaner datasets and instruction-following datasets shuffled together, like in MiniCPM. The learning rate schedule for the 2-stage pretraining was Warmup-Stable-Decay (WSD). During the first stage, the learning rate schedule had a linear warmup for about 8 billion tokens to a peak learning rate of 1e-4 (note: with the Lion optimizer, the learning rate had to be about 10 times smaller than with the commonly used AdamW), followed by a stable phase where the rate of 1e-4 was kept constant. During the second stage, the learning rate schedule had a linear decay from 1e-4 to 6e-6 for the first 7 billion tokens, followed by a stable phase for the remaining tokens.
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In the first stage, the model was trained for 118 billion tokens, which is about three epochs of the first-stage training data, inspired by the findings of [
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Thanks to the WSD learning rate schedule, you can more easily experiment with different first-stage model checkpoints. For example, you could apply the second-stage training on an earlier checkpoint or continue pretraining further before the second stage. Model checkpoints were pushed to this repository every 100,000 training steps (approximately 13 billion tokens).
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0-shot results:
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| Benchmark
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|:---------------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:----------|:----------|:----------------------|
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| Analogies
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| Arithmetic
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| Cause and Effect
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| Emotions
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| Empirical Judgements
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| General Knowledge
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| HHH Alignment
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| Intent Recognition
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| Misconceptions
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| Paraphrase
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| Sentence Ambiguity
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| Similarities Abstraction
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| **Non-Arithmetic Average** | **47.55**
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| **Overall Average**
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3-shot results:
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| Benchmark
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|:---------------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:----------|:----------|:----------------------|
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| Analogies
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| Arithmetic
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| Cause and Effect
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| Emotions
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| Empirical Judgements
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| General Knowledge
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| HHH Alignment
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| Intent Recognition
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| Misconceptions
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| Paraphrase
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| Sentence Ambiguity
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| Similarities Abstraction
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| **Non-Arithmetic Average** | **48.48**
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| **Overall Average**
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As we can see, Ahma 7B base model has bad arithmetic performance but in non-arithmetic tasks it clearly outperforms same sized models like the FinGPT 8B and Viking 7B, especially in 0-shot usage. Ahma 7B base model is even on-par with the 5X larger Poro 34B model, in non-arithmetic tasks in 0-shot usage. This result might be attributed to Ahma's 2-stage pretraining and the inclusion of instruct-following examples during the pretraining phase.
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Single-turn results:
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| Benchmark
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|:--------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|
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| Coding
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| Extraction
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| Humanities
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| Math
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| Reasoning
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| Roleplay
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| STEM
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| Writing
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| **Overall Average** | **3.68**
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Multi-turn results:
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| Benchmark
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|:--------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:--------------|
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| Coding
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| Extraction
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| Humanities
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| Math
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| Reasoning
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| Roleplay
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| STEM
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| Writing
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| **Overall Average** | **2.92**
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As we can see, Ahma 7B base model struggles with multi-turn examples, as expected, since it has only been pretrained with single-turn instruction following examples. In addition, coding performance was expectedly poor because the Ahma 7B model is not trained with code data. In single-turn setting, Ahma 7B beats both the Ahma 3B base and Instruct-tuned versions, demonstrating greater base capability to be further improved with Instruct-tuning.
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Feel free to contact us for more details 🤗
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---
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datasets:
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- Finnish-NLP/CulturaX_fi_cleaned
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- Finnish-NLP/HPLT_1.2_fi_cleaned
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- Finnish-NLP/wikipedia_20231101_fi_cleaned
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- Finnish-NLP/Reddit_fi_2006_2022
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- intfloat/multilingual_cc_news
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language:
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- fi
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- finnish
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- llama
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library_name: transformers
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---
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# Ahma-7B for Finnish
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Ahma-7B is a 7B parameter decoder-only transformer model based on Meta's Llama (v1) architecture, pretrained from scratch on the Finnish language. Its development was informed by the research presented in the paper [Scaling Data-Constrained Language Models](https://huggingface.co/papers/2305.16264). The original Llama model architecture was introduced in
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[this paper](https://arxiv.org/abs/2302.13971)
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and first released at [this page](https://github.com/facebookresearch/llama).
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There are two different sized base Ahma models both pretrained from scratch, Ahma-3B for 139B tokens and Ahma-7B for 149B tokens:
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| Model | Context length | Layers | Dim | Heads | Params |
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|:--------------------------------------------------------------------------------|:---------------|:-------|:-----|:------|:-------|
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| [Ahma-3B](https://huggingface.co/Finnish-NLP/Ahma-3B) | 2048 | 26 | 3200 | 32 | 3.6B |
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| [Ahma-7B](https://huggingface.co/Finnish-NLP/Ahma-7B) | 2048 | 32 | 4096 | 32 | 7.0B |
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And two instruct-tuned versions:
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| Model | Context length | Layers | Dim | Heads | Params |
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|:--------------------------------------------------------------------------------|:---------------|:-------|:-----|:------|:-------|
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| [Ahma-3B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-3B-Instruct) | 2048 | 26 | 3200 | 32 | 3.6B |
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| [Ahma-7B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-7B-Instruct) | 2048 | 32 | 4096 | 32 | 7.0B |
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## Paper Abstract
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The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at this https URL .
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## Intended uses & limitations
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def format_prompt(prompt: str) -> str:
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prompt = f" [INST] <<SYS>>
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{system_prompt.strip()}
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<</SYS>>
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{prompt.strip()} [/INST] "
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return prompt
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The final training dataset had 23 billion words (calculated with regex "\w+") and the evaluation dataset had 23 million words. After tokenization, the training dataset had 41 billion tokens and the evaluation dataset had 40 million tokens. For the 2-stage pretraining, training datasets are divided as follows:
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The first stage:
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|Dataset | Words | Ratio |
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|:-----------------------------|:------------|:-------------|
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|CulturaX | 12.820B | 59.88% |
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|HPLT v1.2 | 5.034B | 23.51% |
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|Suomi24 | 3.018B | 14.09% |
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|Reddit | 0.141B | 0.66% |
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|CC-News | 0.311B | 1.45% |
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|FI news corpus | 0.004B | 0.02% |
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|Project Lönnrot | 0.083B | 0.39% |
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|**TOTAL** | **21.410B** | **100.0%** |
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The second stage:
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|Dataset | Words | Ratio |
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|:--------------------------------------------------------------|:------------|:------------|
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|CulturaX (cleaner sample using KenLM perplexity score) | 2.252B | 55.48% |
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|Wikipedia | 0.095B | 2.34% |
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|STT | 0.253B | 6.23% |
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|Yle | 0.212B | 5.22% |
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|Finnish parliament speeches | 0.021B | 0.52% |
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|Finnish higher education public theses | 0.855B | 21.07% |
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|Finnish instruction-following datasets (note: 2X upsampled) | 0.371B | 9.14% |
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|**TOTAL** | **4.059B** | **100.0%** |
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## Training procedure
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The 2-stage pretraining approach was inspired by [MiniCPM](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20) findings. For the first stage (79% of the entire training), we used noisier web-scraped datasets. For the second stage (21% of the entire training), we primarily used cleaner datasets and instruction-following datasets shuffled together, like in MiniCPM. The learning rate schedule for the 2-stage pretraining was Warmup-Stable-Decay (WSD). During the first stage, the learning rate schedule had a linear warmup for about 8 billion tokens to a peak learning rate of 1e-4 (note: with the Lion optimizer, the learning rate had to be about 10 times smaller than with the commonly used AdamW), followed by a stable phase where the rate of 1e-4 was kept constant. During the second stage, the learning rate schedule had a linear decay from 1e-4 to 6e-6 for the first 7 billion tokens, followed by a stable phase for the remaining tokens.
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In the first stage, the model was trained for 118 billion tokens, which is about three epochs of the first-stage training data, inspired by the findings of [Scaling Data-Constrained Language Models](https://huggingface.co/papers/2305.16264). In the second stage, the model was trained for 31 billion tokens, which is close to five epochs of the second-stage training data.
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Thanks to the WSD learning rate schedule, you can more easily experiment with different first-stage model checkpoints. For example, you could apply the second-stage training on an earlier checkpoint or continue pretraining further before the second stage. Model checkpoints were pushed to this repository every 100,000 training steps (approximately 13 billion tokens).
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0-shot results:
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| Benchmark | Ahma 3B base (instruct prompt format) | Ahma 3B Instruct (instruct prompt format) | Ahma 7B base (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) | FinGPT 8B | Viking 7B | Poro 34B (8bit quant) |
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|:---------------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:----------|:----------|:----------------------|
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| Analogies | 50.77 | 48.46 | 56.92 | 41.54 | 49.23 | 40.00 | 54.62 |
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| Arithmetic | 27.64 | 22.14 | 11.50 | 14.70 | 33.15 | 30.16 | 30.34 |
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| Cause and Effect | 59.48 | 58.82 | 59.48 | 53.60 | 66.01 | 58.82 | 62.74 |
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| Emotions | 36.25 | 28.12 | 36.25 | 27.50 | 22.50 | 26.25 | 35.63 |
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| Empirical Judgements | 33.33 | 35.35 | 33.33 | 33.33 | 27.27 | 33.33 | 49.49 |
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| General Knowledge | 44.29 | 48.57 | 51.43 | 37.14 | 40.00 | 24.29 | 51.43 |
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| HHH Alignment | 42.09 | 41.66 | 44.23 | 43.22 | 41.81 | 42.51 | 42.92 |
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| Intent Recognition | 24.42 | 26.16 | 43.64 | 56.94 | 17.49 | 22.40 | 68.35 |
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| Misconceptions | 46.27 | 47.01 | 46.27 | 47.01 | 53.73 | 53.73 | 52.24 |
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| Paraphrase | 59.50 | 73.00 | 67.00 | 70.50 | 51.00 | 50.00 | 51.00 |
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| Sentence Ambiguity | 53.33 | 65.00 | 60.00 | 63.33 | 51.67 | 48.33 | 50.00 |
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| Similarities Abstraction | 65.79 | 68.42 | 71.05 | 61.84 | 60.53 | 65.79 | 60.53 |
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| **Non-Arithmetic Average** | **47.55** | **48.95** | **51.33** | **48.30** | **46.17** | **44.42** | **52.08** |
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| **Overall Average** | **36.49** | **34.06** | **29.20** | **29.64** | **38.93** | **36.50** | **40.00** |
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3-shot results:
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| Benchmark | Ahma 3B base (instruct prompt format) | Ahma 3B Instruct (instruct prompt format) | Ahma 7B base (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) | FinGPT 8B | Viking 7B | Poro 34B (8bit quant) |
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|:---------------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:----------|:----------|:----------------------|
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| Analogies | 50.77 | 49.23 | 49.23 | 43.08 | 40.77 | 54.62 | 76.92 |
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| Arithmetic | 38.38 | 43.89 | 20.88 | 26.81 | 43.63 | 45.78 | 53.68 |
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| Cause and Effect | 60.78 | 64.71 | 66.01 | 62.74 | 64.05 | 58.17 | 67.32 |
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| Emotions | 30.00 | 41.25 | 30.00 | 53.75 | 44.37 | 48.13 | 56.87 |
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| Empirical Judgements | 46.46 | 44.44 | 39.39 | 39.39 | 32.32 | 43.43 | 63.64 |
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| General Knowledge | 47.14 | 40.00 | 27.14 | 44.29 | 54.29 | 28.57 | 74.29 |
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| HHH Alignment | 43.53 | 44.80 | 43.80 | 45.09 | 45.39 | 44.80 | 46.07 |
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| Intent Recognition | 20.52 | 44.22 | 36.42 | 39.02 | 51.45 | 58.82 | 83.67 |
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| Misconceptions | 50.75 | 52.24 | 46.27 | 51.49 | 52.99 | 46.27 | 52.99 |
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| Paraphrase | 50.50 | 58.50 | 57.50 | 65.00 | 53.00 | 54.50 | 55.00 |
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| Sentence Ambiguity | 53.33 | 48.33 | 53.33 | 51.67 | 51.67 | 53.33 | 66.67 |
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| Similarities Abstraction | 69.74 | 72.37 | 72.37 | 69.74 | 64.47 | 73.68 | 75.00 |
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| **Non-Arithmetic Average** | **48.48** | **51.49** | **49.05** | **51.63** | **51.19** | **50.94** | **61.96** |
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| **Overall Average** | **42.87** | **47.27** | **33.41** | **37.84** | **46.99** | **48.07** | **57.36** |
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As we can see, Ahma 7B base model has bad arithmetic performance but in non-arithmetic tasks it clearly outperforms same sized models like the FinGPT 8B and Viking 7B, especially in 0-shot usage. Ahma 7B base model is even on-par with the 5X larger Poro 34B model, in non-arithmetic tasks in 0-shot usage. This result might be attributed to Ahma's 2-stage pretraining and the inclusion of instruct-following examples during the pretraining phase.
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Single-turn results:
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| Benchmark | Ahma 3B base (instruct prompt format) | Ahma 3B Instruct (instruct prompt format) | Ahma 7B base (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) |
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|:--------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|
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| Coding | 1.00 | 1.00 | 1.70 | 1.10 |
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| Extraction | 2.00 | 1.30 | 3.10 | 3.00 |
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| 266 |
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| Humanities | 4.05 | 6.20 | 6.60 | 8.00 |
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| Math | 3.00 | 3.20 | 3.90 | 2.90 |
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| 268 |
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| Reasoning | 2.90 | 4.60 | 3.70 | 5.70 |
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| 269 |
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| Roleplay | 4.80 | 6.50 | 6.60 | 7.20 |
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| STEM | 5.10 | 5.95 | 6.75 | 7.30 |
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| 271 |
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| Writing | 6.60 | 9.00 | 7.10 | 8.80 |
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| **Overall Average** | **3.68** | **4.72** | **4.93** | **5.50** |
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Multi-turn results:
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| Benchmark | Ahma 3B base (instruct prompt format) | Ahma 3B Instruct (instruct prompt format) | Ahma 7B base (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) | Poro 34B Chat |
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| 277 |
|:--------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|:------------------------------------------|:--------------|
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| Coding | 1.00 | 1.00 | 1.40 | 1.05 | 3.70 |
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| 279 |
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| Extraction | 1.55 | 1.15 | 2.05 | 2.65 | 6.37 |
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| 280 |
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| Humanities | 3.25 | 6.20 | 4.95 | 7.85 | 9.25 |
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| 281 |
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| Math | 2.20 | 2.70 | 2.50 | 2.40 | 1.20 |
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| 282 |
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| Reasoning | 2.45 | 3.50 | 2.55 | 4.50 | 4.35 |
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| Roleplay | 4.90 | 6.40 | 6.35 | 6.60 | 7.35 |
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| STEM | 4.20 | 4.78 | 4.28 | 5.40 | 7.80 |
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| Writing | 3.80 | 6.65 | 4.10 | 6.25 | 8.50 |
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| **Overall Average** | **2.92** | **4.05** | **3.52** | **4.59** | **6.06** |
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As we can see, Ahma 7B base model struggles with multi-turn examples, as expected, since it has only been pretrained with single-turn instruction following examples. In addition, coding performance was expectedly poor because the Ahma 7B model is not trained with code data. In single-turn setting, Ahma 7B beats both the Ahma 3B base and Instruct-tuned versions, demonstrating greater base capability to be further improved with Instruct-tuning.
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