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Model: anakin87/gemma-2-9b-neogenesis-ita Source: Original Platform
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README.md
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---
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license: gemma
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language:
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- it
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- en
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base_model:
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- VAGOsolutions/SauerkrautLM-gemma-2-9b-it
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- mii-llm/argilla-math-preferences-it
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- ruggsea/wsdm2024-cot-dataset
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- anakin87/evol-dpo-ita-reranked
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- mlabonne/orpo-dpo-mix-40k
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---
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<h1>Gemma 2 9B Neogenesis ITA</h1>
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<img src="https://github.com/anakin87/gemma-neogenesis/blob/main/images/gemma_neogenesis_9b.jpeg?raw=true" width="450px">
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Fine-tuned version of [VAGOsolutions/SauerkrautLM-gemma-2-9b-it](https://huggingface.co/VAGOsolutions/SauerkrautLM-gemma-2-9b-it) optimized for better performance in Italian.
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- Good model with 9.24 billion parameters
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- Supports 8k context length
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[GGUF quants](https://huggingface.co/tensorblock/gemma-2-9b-neogenesis-ita-GGUF)
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*Need a smaller model?* Try [gemma-2-2b-neogenesis-ita](https://huggingface.co/anakin87/gemma-2-2b-neogenesis-ita).
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# 🎮 Usage
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[💬🇮🇹 Try the model on Hugging Face Spaces](https://huggingface.co/spaces/anakin87/gemma-2-9b-neogenesis-ita)
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**Text generation with Transformers**
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```python
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import torch
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from transformers import pipeline
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model_id="anakin87/gemma-2-9b-neogenesis-ita"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device="cuda",
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)
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messages = [{"role": "user", "content": "Cos'è l'interesse composto? Spiega in maniera semplice e chiara."}]
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outputs = pipe(messages, max_new_tokens=500)
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print(outputs[0]["generated_text"][1]["content"])
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```
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# 🏆 Evaluation Results
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The model was submitted and evaluated in the [Open Ita LLM Leaderboard](https://huggingface.co/spaces/mii-llm/open_ita_llm_leaderboard), the most popular leaderboard for Italian Language Models.
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| Model | MMLU_IT | ARC_IT | HELLASWAG_IT | Average |
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|-----------------------|---------|--------|--------------|---------|
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| google/gemma-2-9b-it | 65.67 | 55.6 |68.95 | 63.41 |
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| VAGOsolutions/SauerkrautLM-gemma-2-9b-it | 65.76 | **61.25** |72.10 | 66.37 |
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| **anakin87/gemma-2-9b-neogenesis-ita** | **65.82** | **61.25** |**73.29** | **66.79** |
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These results establish this model as a strong 9B model for Italian, outperforming 13-14B models and even surpassing some in the 30-70B range.
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# 🔧 Training details
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The model was fine-tuned using [Hugging Face TRL](https://huggingface.co/docs/trl/index) and applying Direct Preference Optimization.
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I adopted a relatively new technique for parameter-efficient learning: [Spectrum](https://arxiv.org/abs/2406.06623). The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest. Specifically, training focused on the top 20% most informative layers.
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Batch size: 16; learning rate: 1e-6; epochs: 1.
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The training process took approximately 12 hours on a single NVIDIA A100 GPU (80GB VRAM).
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For the training code, see the DPO section in this [📓 Kaggle notebook](https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond), modified to use a different base model, hyperparameters, and no on-policy data.
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# 🗃️ Training data
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The model was trained primarily on Italian data, with a small portion of English data included.
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For Direct Preference Optimization
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- Italian data
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- [mii-llm/argilla-math-preferences-it](https://huggingface.co/datasets/mii-llm/argilla-math-preferences-it)
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- [ruggsea/wsdm2024-cot-dataset](https://huggingface.co/datasets/ruggsea/wsdm2024-cot-dataset)
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- [anakin87/evol-dpo-ita-reranked](https://huggingface.co/datasets/anakin87/evol-dpo-ita-reranked)
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- English data
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- [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k)
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🙏 Thanks to the authors for providing these datasets.
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# 🛡️ Safety
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While this model was not specifically fine-tuned for safety, its selective training with the Spectrum technique helps preserve certain safety features from the original model.
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config.json
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 3584,
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"transformers_version": "4.47.1",
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"use_cache": true,
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"vocab_size": 256000
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}
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34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
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|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
|
||||
size 34362873
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||
size 4241003
|
||||
2014
tokenizer_config.json
Normal file
2014
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user