Instructions to use bharathmuppa/angular-coder-1.5b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bharathmuppa/angular-coder-1.5b-lora with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download bharathmuppa/angular-coder-1.5b-lora --local-dir angular-coder-1.5b-lora
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Upload LoRA adapter
Browse files- adapter_config.json +59 -0
- adapters.safetensors +3 -0
adapter_config.json
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{
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"adapter_path": "adapters",
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"batch_size": 2,
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"clear_cache_threshold": 0,
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"config": "configs/lora.yaml",
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"data": "data/sft",
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"fine_tune_type": "lora",
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"grad_accumulation_steps": 2,
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"grad_checkpoint": true,
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"iters": 1400,
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"learning_rate": 3e-05,
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"lora_parameters": {
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"rank": 16,
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"scale": 16.0,
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"dropout": 0.05,
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"keys": [
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"self_attn.q_proj",
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"self_attn.k_proj",
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"self_attn.v_proj",
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"self_attn.o_proj",
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"mlp.gate_proj",
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"mlp.up_proj",
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"mlp.down_proj"
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]
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},
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"lr_schedule": {
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"name": "cosine_decay",
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"warmup": 50,
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"arguments": [
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3e-05,
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1400,
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1e-06
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]
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},
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"mask_prompt": true,
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"max_seq_length": 1024,
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"model": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
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"num_layers": 16,
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"optimizer": "adam",
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"optimizer_config": {
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"adam": {},
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"adamw": {},
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"muon": {},
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"sgd": {},
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"adafactor": {}
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},
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"project_name": null,
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"report_to": null,
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"resume_adapter_file": null,
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"save_every": 200,
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"seed": 7,
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"steps_per_eval": 100,
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"steps_per_report": 10,
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"test": false,
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"test_batches": 500,
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"train": true,
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"trust_remote_code": false,
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"val_batches": 25
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}
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adapters.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2a3337277057e238913f76a79f97340bf359d0ae19377fc31607acc7fff31f7e
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size 42229570
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