Image-Text-to-Text
PEFT
Safetensors
android
android-world
ui-automation
accessibility
vision-language
lora
reinforcement-learning
Instructions to use dmitchelljackson/cerebellum-qwen35-history-actions-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dmitchelljackson/cerebellum-qwen35-history-actions-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B") model = PeftModel.from_pretrained(base_model, "dmitchelljackson/cerebellum-qwen35-history-actions-lora") - Notebooks
- Google Colab
- Kaggle
File size: 914 Bytes
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"model_name": "Qwen/Qwen3.5-0.8B",
"init_adapter": "checkpoints/qwen35_history_actions_eosK_aw2_accum3_from_step210/current",
"out": "checkpoints/qwen35_history_actions_eosK_aw2_accum4_from_step140",
"steps": 1000,
"quota": "tp=5,k=2,scroll=2,wait=2,system=1",
"lr": 0.0001,
"grad_accum": 4,
"optim": "paged_adamw_8bit",
"action_weight": 2.0,
"margin_weight": 0.25,
"margin_target": 8.0,
"history_len": 4,
"min_history": 4,
"hist_width": 232,
"hist_gutter": 16,
"curr_width": 464,
"curr_gutter": 32,
"max_seq_len": 2600,
"max_raw_tree_chars": 4000,
"max_pix_rows": 12000,
"max_mm_tokens": 2600,
"save_every": 5,
"log_every": 1,
"log_shapes": true,
"skip_oom": true,
"shards": [
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"seed": 3396423176,
"fresh_seed": true
} |