Instructions to use dobleuber/gemma-2-2B-it-thinking-function_calling-V0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dobleuber/gemma-2-2B-it-thinking-function_calling-V0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dobleuber/gemma-2-2B-it-thinking-function_calling-V0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dobleuber/gemma-2-2B-it-thinking-function_calling-V0
Browse files- README.md +3 -3
- adapter_config.json +38 -38
- adapter_model.safetensors +1 -1
- added_tokens.json +10 -0
- runs/Feb23_12-38-32_3d7aca86abae/events.out.tfevents.1740314333.3d7aca86abae.7058.0 +3 -0
- special_tokens_map.json +42 -36
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
- training_args.bin +1 -1
README.md
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### Framework versions
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- TRL: 0.15.1
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- Transformers: 4.
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- Pytorch: 2.5.1+
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- Datasets: 3.3.2
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- Tokenizers: 0.21.0
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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### Framework versions
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- TRL: 0.15.1
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- Transformers: 4.48.3
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- Pytorch: 2.5.1+cu124
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- Datasets: 3.3.2
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- Tokenizers: 0.21.0
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "google/gemma-2-2b-it",
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "google/gemma-2-2b-it",
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"up_proj",
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"k_proj",
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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added_tokens.json
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runs/Feb23_12-38-32_3d7aca86abae/events.out.tfevents.1740314333.3d7aca86abae.7058.0
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special_tokens_map.json
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tokenizer.model
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size 4241003
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tokenizer_config.json
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training_args.bin
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