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Model: colinglab/CLASS_IT-140M Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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<!--
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## Model Details
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-->
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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CLASS-IT is a 140M parameter language model based on the LLaMA architecture.
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The model is first pre-trained for 8 epochs on a cleaned version of the BabyLM Challenge strict track dataset.
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After pre-training, the model is instruction-tuned on two additional datasets (8.7M words total) for 10 epochs :
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- a conversational dataset derived from Switchboard, and
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- an educational dataset based on an augmented version of Simple English Wikipedia (to be released soon).
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<!-- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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-->
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<!-- Provide the basic links for the model. -->
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<!--
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!--
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!--
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!--
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!--
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!--
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-->
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<!--
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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<!--
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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<!--
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#### Preprocessing [optional]
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[More Information Needed]
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<!--
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!--
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!--
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[More Information Needed]
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-->
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## Evaluation
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The model has been submitted to the 2025 BabyLM Challenge – Interaction Track:
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https://huggingface.co/spaces/BabyLM-community/babylm-leaderboard-2025-all-tasks
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## Citation
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This model was introduced in the paper:
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**“CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs”**
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*(Capone, Bondielli & Lenci, BabyLM Challange 2025)*
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📄 [ArXiv: 2510.25364](https://arxiv.org/abs/2510.25364)
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**Cite as (BibTeX)**:
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```
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@inproceedings{capone-etal-2025-class,
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title = "{CLASS}-{IT}: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for {B}aby{LM}s",
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author = "Capone, Luca and
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Bondielli, Alessandro and
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Lenci, Alessandro",
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editor = "Charpentier, Lucas and
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Choshen, Leshem and
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Cotterell, Ryan and
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Gul, Mustafa Omer and
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Hu, Michael Y. and
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Liu, Jing and
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Jumelet, Jaap and
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Linzen, Tal and
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Mueller, Aaron and
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Ross, Candace and
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Shah, Raj Sanjay and
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Warstadt, Alex and
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Wilcox, Ethan Gotlieb and
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Williams, Adina",
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booktitle = "Proceedings of the First BabyLM Workshop",
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month = nov,
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year = "2025",
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address = "Suzhou, China",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2025.babylm-main.30/",
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pages = "436--444",
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ISBN = "TODO"
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}
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```
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<!--
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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<!--
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!--
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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<!--
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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<!--
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-->
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<!--
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!--
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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<!--
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]-->
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38
config.json
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config.json
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{
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"_name_or_path": "/extra/luca.capone/babylmchallange25/ituned_models/llama117M_it_switch_wiki/results_best_switch_wiki",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 1,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 704,
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"initializer_range": 0.02,
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"intermediate_size": 2816,
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"max_position_embeddings": 6144,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 11,
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"num_hidden_layers": 12,
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"num_key_value_heads": 11,
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"pad_token_id": 3,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"factor": 2.0,
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"rope_type": "linear",
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"type": "linear"
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},
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"transformers_version": "4.49.0",
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"unk_token_id": 2,
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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}
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size 470827712
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special_tokens_map.json
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{
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"additional_special_tokens": [
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159046
tokenizer.json
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159046
tokenizer.json
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82
tokenizer_config.json
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tokenizer_config.json
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{
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"tokenizer_class": "PreTrainedTokenizer",
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"unk_token": "<unk>"
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}
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