Image-Text-to-Text
PEFT
Safetensors
English
Chinese
decision-model
judgment
classification
vision-language
jev
systemone
qwen3.5
lora
vev
Eval Results (legacy)
Instructions to use CountingSheep/vev-9b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CountingSheep/vev-9b-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download vev.json from CountingSheep/vev-9b-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.13 kB
-
https://huggingface.co/CountingSheep/vev-9b-lora/resolve/main/vev.json
- Command line
-
hf download hf://CountingSheep/vev-9b-lora/vev.json
-
curl -L -o vev.json https://huggingface.co/CountingSheep/vev-9b-lora/resolve/main/vev.json
1.13 kB
| { | |
| "base_model": "Qwen/Qwen3.5-9B", | |
| "vev_version": "0.1.0", | |
| "train_args": { | |
| "build": "v1-research", | |
| "base": "Qwen/Qwen3.5-9B", | |
| "out": "train", | |
| "limit": 100000, | |
| "sources": null, | |
| "source_weights": null, | |
| "max_steps": 2500, | |
| "lr": 5e-05, | |
| "head_lr": 0.0, | |
| "lora_r": 16, | |
| "lora_alpha": 0, | |
| "lora_dropout": 0.05, | |
| "lora_targets": "all", | |
| "dp": 256, | |
| "prior": true, | |
| "anchor_kl": 0.3, | |
| "anchor_build": "anchor-v3", | |
| "anchor_weight": 2.0, | |
| "anchor_sym": false, | |
| "consistency": 0.0, | |
| "consistency_frac": 0.5, | |
| "micro_tokens": 16384, | |
| "accum": 4, | |
| "max_q": 4, | |
| "max_row_tokens": 4096, | |
| "max_pixels": 1048576, | |
| "brier": 0.1, | |
| "score_w": 0.05, | |
| "weight_decay": 0.0, | |
| "grad_clip": 1.0, | |
| "warmup_pct": 0.1, | |
| "no_augment": false, | |
| "no_gradient_checkpointing": false, | |
| "dtype": "bf16", | |
| "attn": null, | |
| "workers": 8, | |
| "eval_every": 250, | |
| "eval_rows": 1000, | |
| "eval_records": 2000, | |
| "save_every": 250, | |
| "log_every": 10, | |
| "seed": 0, | |
| "resume": null, | |
| "device": "cuda" | |
| }, | |
| "base": "Qwen/Qwen3.5-9B" | |
| } |