Instructions to use ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - PEFT
How to use ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "ansarzeinulla/Qwen2.5-1.5B-Nogai-LoRA" --prompt "Once upon a time"
- Atomic Chat
| { | |
| "adapter_path": "adapters/qwen_1.5b_nogai", | |
| "batch_size": 2, | |
| "clear_cache_threshold": 0, | |
| "config": null, | |
| "data": "./nogai_data", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 1, | |
| "grad_checkpoint": false, | |
| "iters": 2500, | |
| "learning_rate": 0.0002, | |
| "lora_parameters": { | |
| "rank": 8, | |
| "dropout": 0.0, | |
| "scale": 20.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": false, | |
| "max_seq_length": 512, | |
| "model": "./local_qwen_1.5B", | |
| "num_layers": 16, | |
| "optimizer": "adam", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": null, | |
| "save_every": 500, | |
| "seed": 0, | |
| "steps_per_eval": 200, | |
| "steps_per_report": 10, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "val_batches": 25 | |
| } |