pangram/editlens_iclr
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How to use bingbangboom/qwen3-0.6B-holmes-nt with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="bingbangboom/qwen3-0.6B-holmes-nt") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("bingbangboom/qwen3-0.6B-holmes-nt")
model = AutoModelForMultimodalLM.from_pretrained("bingbangboom/qwen3-0.6B-holmes-nt")How to use bingbangboom/qwen3-0.6B-holmes-nt with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bingbangboom/qwen3-0.6B-holmes-nt to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bingbangboom/qwen3-0.6B-holmes-nt to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bingbangboom/qwen3-0.6B-holmes-nt to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="bingbangboom/qwen3-0.6B-holmes-nt",
max_seq_length=2048,
)Qwen3-0.6B SFT'd on text→text_type pairs from pangram/editlens_iclr to output one of three labels: human_written, ai_edited, or ai_generated.
No thinking mode, direct label output only.
This is a WIP and requires review. See the benchmark script for methodology. If you find any mistakes, please let me know. Refer the official EditLens Repo and their paper for more context.
| Parameter | Value |
|---|---|
| Base model | unsloth/qwen3-0.6b-unsloth-bnb-4bit |
| Epochs | 1 (1875 steps) |
| Learning rate | 5e-5 (linear schedule) |
| Warmup steps | 50 |
| Optimizer | AdamW 8-bit |
| Per-device batch size | 2 |
| Gradient accumulation | 16 |
| Effective batch size | 32 |
| Max sequence length | 2048 |
| LoRA rank / alpha | 16 / 16 |
| Seed | 3407 |
Trained with Unsloth and HuggingFace TRL.
Paper baselines from EditLens (ICLR 2026, Table 2). Dark-edged bars = local run(Open Source).
Paper baselines from Table 1, Section 4.2
Full benchmark pipeline and raw predictions: revalidate/