Instructions to use michlea/HidatoQwenModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michlea/HidatoQwenModel with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("michlea/HidatoQwenModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Training in progress, step 150, checkpoint
Browse files- last-checkpoint/adapter_config.json +9 -5
- last-checkpoint/adapter_model.safetensors +1 -1
- last-checkpoint/optimizer.pt +1 -1
- last-checkpoint/rng_state.pth +1 -1
- last-checkpoint/scaler.pt +1 -1
- last-checkpoint/scheduler.pt +1 -1
- last-checkpoint/trainer_state.json +0 -0
- last-checkpoint/training_args.bin +2 -2
last-checkpoint/adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping":
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"base_model_name_or_path": "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit",
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"bias": "none",
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"corda_config": null,
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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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"v_proj",
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"o_proj",
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"gate_proj",
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"down_proj",
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"k_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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{
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"alpha_pattern": {},
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"auto_mapping": {
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"base_model_class": "Qwen2ForCausalLM",
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"parent_library": "transformers.models.qwen2.modeling_qwen2",
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"unsloth_fixed": true
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},
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"base_model_name_or_path": "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit",
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"bias": "none",
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"corda_config": null,
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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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"down_proj",
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"k_proj",
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"v_proj",
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"o_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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last-checkpoint/adapter_model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scaler.pt
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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last-checkpoint/training_args.bin
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