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
Download adapter_config.json from bharathmuppa/angular-coder-1.5b-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/bharathmuppa/angular-coder-1.5b-lora/resolve/main/adapter_config.json
- Command line
-
hf download hf://bharathmuppa/angular-coder-1.5b-lora/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/bharathmuppa/angular-coder-1.5b-lora/resolve/main/adapter_config.json
1.38 kB
| { | |
| "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 | |
| } |