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--- |
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base_model: |
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- tiiuae/falcon-11B |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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- lazymergekit |
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- tiiuae/falcon-11B |
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license: apache-2.0 |
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language: |
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- 'no' |
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--- |
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## Why prune? |
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Even though [Falcon-11B](https://huggingface.co./tiiuae/falcon-11B) is trained on 5T tokens, it is still undertrained, as can be seen by this graph: |
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 |
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This is why the choice is made to prune 50% of the layers. |
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Note that \~1B of continued pre-training (\~1M rows of 1k tokens) is still required to restore the perplexity of this model in the desired language. |
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I'm planning on doing that for certain languages, depending on how much compute will be available. |
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# sliced |
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). |
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## Merge Details |
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### Merge Method |
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This model was pruned using the passthrough merge method. |
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### Models Merged |
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The following models were included in the merge: |
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* [tiiuae/falcon-11B](https://huggingface.co./tiiuae/falcon-11B) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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slices: |
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- sources: |
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- model: tiiuae/falcon-11B |
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layer_range: [0, 24] |
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- sources: |
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- model: tiiuae/falcon-11B |
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layer_range: [55, 59] |
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merge_method: passthrough |
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dtype: bfloat16 |
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``` |
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[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the wikimedia/wikipedia Norwegian (no) subset by investigating layer similarity with 2000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size. |
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 |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import transformers |
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import torch |
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model = "ssmits/Falcon2-5.5B-Norwegian" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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torch_dtype=torch.bfloat16, |
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) |
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sequences = pipeline( |
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"Can you explain the concepts of Quantum Computing?", |
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max_length=200, |
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do_sample=True, |
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top_k=10, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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for seq in sequences: |
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print(f"Result: {seq['generated_text']}") |
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``` |
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💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** |
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For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co./blog/falcon). |
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## Direct Use |
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Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) |
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## Out-of-Scope Use |
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Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. |
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## Bias, Risks, and Limitations |
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Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. |
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## Recommendations |
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We recommend users of Falcon2-5.5B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. |
