Instructions to use fundthmcalculus/LFM2-350M-hugcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fundthmcalculus/LFM2-350M-hugcoder with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/LFM2-350M") model = PeftModel.from_pretrained(base_model, "fundthmcalculus/LFM2-350M-hugcoder") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 50
Browse files- adapter_config.json +7 -7
- adapter_model.safetensors +2 -2
- training_args.bin +1 -1
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": 0.1,
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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":
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"w2",
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"w1",
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"in_proj",
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"
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"w3",
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"q_proj",
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"k_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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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 256,
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"lora_bias": false,
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"lora_dropout": 0.1,
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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": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"w3",
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"k_proj",
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"q_proj",
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"v_proj",
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"out_proj",
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"w2",
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"in_proj",
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"w1"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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adapter_model.safetensors
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size 95969208
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 5841
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