Instructions to use underscore2/llama3-8b-bluesky-engagement-kto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underscore2/llama3-8b-bluesky-engagement-kto with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("underscore2/llama3-8b-bluesky-engagement-kto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- adapter_config.json +6 -8
- adapter_model.safetensors +2 -2
adapter_config.json
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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":
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"lora_bias": false,
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"lora_dropout": 0,
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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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"
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"o_proj",
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"
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"down_proj",
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"gate_proj",
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"q_proj",
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"up_proj",
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"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": true
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}
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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": 128,
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"lora_bias": false,
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"lora_dropout": 0,
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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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"o_proj",
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"k_proj",
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"q_proj",
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"up_proj",
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"v_proj",
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"gate_proj",
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"down_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": true
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}
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adapter_model.safetensors
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size
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version https://git-lfs.github.com/spec/v1
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size 167832240
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