Instructions to use ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000: direct link, hf CLI and curl.
- Browser
- Download file 889 Bytes
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https://huggingface.co/ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000/resolve/main/README.md
- Command line
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hf download hf://ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000/README.md
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curl -L -o README.md https://huggingface.co/ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000/resolve/main/README.md
889 Bytes
metadata
base_model: Qwen/Qwen3-4B
library_name: peft
LoRA Adapter for SAE Introspection
This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
Base Model
- Base Model:
Qwen/Qwen3-4B - Adapter Type: LoRA
- Task: SAE Feature Introspection
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "ceselder/MLAO-Qwen3-4B-3L-1N-distinct-tokens-step-5000")
Training Details
This adapter was trained using the lightweight SAE introspection training script to help the model understand and explain SAE features through activation steering.