Instructions to use 0xsoftboi/gemma-4-e2b-it-kali-nethunter-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use 0xsoftboi/gemma-4-e2b-it-kali-nethunter-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("0xsoftboi/gemma-4-e2b-it-kali-nethunter-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use 0xsoftboi/gemma-4-e2b-it-kali-nethunter-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 "0xsoftboi/gemma-4-e2b-it-kali-nethunter-lora" --prompt "Once upon a time"
File size: 1,013 Bytes
d1658b9 | 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 | {
"adapter_path": "/tmp/g4-finetune-adapters",
"batch_size": 1,
"clear_cache_threshold": 0,
"config": "/tmp/lora_config.yaml",
"data": "/tmp/OnePlus-Kali/g4-finetune/data",
"fine_tune_type": "lora",
"grad_accumulation_steps": 1,
"grad_checkpoint": true,
"iters": 200,
"learning_rate": 1e-05,
"lora_parameters": {
"rank": 8,
"alpha": 16,
"dropout": 0.05,
"scale": 10.0
},
"lr_schedule": null,
"mask_prompt": true,
"max_seq_length": 1024,
"model": "/tmp/gemma-4-e2b-it-4bit",
"num_layers": 4,
"optimizer": "adam",
"optimizer_config": {
"adam": {},
"adamw": {},
"muon": {},
"sgd": {},
"adafactor": {}
},
"project_name": null,
"report_to": null,
"resume_adapter_file": null,
"save_every": 50,
"seed": 0,
"steps_per_eval": 200,
"steps_per_report": 10,
"test": false,
"test_batches": 500,
"train": true,
"val_batches": 25
} |