Instructions to use louisguthmann/qwen3.5-2b-shellcommand-linux-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use louisguthmann/qwen3.5-2b-shellcommand-linux-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-2B") model = PeftModel.from_pretrained(base_model, "louisguthmann/qwen3.5-2b-shellcommand-linux-lora") - Notebooks
- Google Colab
- Kaggle
File size: 452 Bytes
e897220 432b303 e897220 432b303 e897220 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"model": "/root/bitnet-nl2sh/output/autoresearch_proxy_qwen35_2b/repair_v3b_full_v1/qwen35_2b_batch8_repair_v3b_full_v1/model",
"base_model": "Qwen/Qwen3.5-2B",
"test_file": "/root/bitnet-nl2sh/output/data/nl2sh_test_raw.jsonl",
"rows": 100,
"primary_exact": 20,
"alt_exact": 7,
"any_exact": 25,
"parse_ok": 98,
"primary_exact_rate": 0.2,
"any_exact_rate": 0.25,
"parse_ok_rate": 0.98,
"avg_gen_seconds_per_example": 0.6252
}
|