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
File size: 1,375 Bytes
7ced196 a3e9c1e 7ced196 a3e9c1e 7ced196 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"adapter_path": "adapters",
"batch_size": 2,
"clear_cache_threshold": 0,
"config": "configs/lora.yaml",
"data": "data/sft",
"fine_tune_type": "lora",
"grad_accumulation_steps": 2,
"grad_checkpoint": true,
"iters": 1700,
"learning_rate": 3e-05,
"lora_parameters": {
"rank": 16,
"scale": 16.0,
"dropout": 0.05,
"keys": [
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
"self_attn.o_proj",
"mlp.gate_proj",
"mlp.up_proj",
"mlp.down_proj"
]
},
"lr_schedule": {
"name": "cosine_decay",
"warmup": 50,
"arguments": [
3e-05,
1700,
1e-06
]
},
"mask_prompt": true,
"max_seq_length": 1024,
"model": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
"num_layers": 16,
"optimizer": "adam",
"optimizer_config": {
"adam": {},
"adamw": {},
"muon": {},
"sgd": {},
"adafactor": {}
},
"project_name": null,
"report_to": null,
"resume_adapter_file": null,
"save_every": 200,
"seed": 7,
"steps_per_eval": 100,
"steps_per_report": 10,
"test": false,
"test_batches": 500,
"train": true,
"trust_remote_code": false,
"val_batches": 25
} |