dfc-D8k-excl10-k160

dfc-D8k-excl10-k160

A DFC sparse crosscoder trained to compare layer-13 activations between:

• Model A (ToolRL): chengq9/ToolRL-Qwen2.5-3B — fine-tuned with tool-use reinforcement learning
• Model B (Base): Qwen/Qwen2.5-3B — vanilla base model

What is this?

This model learns a sparse dictionary of features from the internal representations of two language models. By comparing which features activate for which model, we can identify:

• What the ToolRL fine-tuning changed (A-exclusive features)
• What remained the same (shared features)
• What the base model does that ToolRL suppressed (B-exclusive features)

Model Architecture

Dedicated Feature CrossCoder with partitioned dictionary (0.10/0.10 A/B exclusive)

How it works

1. Encode: Takes stacked activations (batch, 2, 2048) from both models, applies per-model encoder weights, sums across models, and selects the top-160 features via ReLU + top-k.
2. Decode: Reconstructs per-model activations from the sparse feature vector using per-model decoder weights.
3. Partition masks (DFC only): Hard binary masks zero out encoder/decoder weights to enforce that exclusive features cannot be used by the wrong model.

Training

Training Data

• FineWeb: ~40,000 general web text samples (from HuggingFaceFW/fineweb sample-10BT)
• ToolRL: ~40,000 tool-use conversation samples (from emrecanacikgoz/ToolRL, cycled)
• Activations extracted from layer 13, last token per sample
• Both datasets concatenated and z-score normalized

Usage

Quick Start

import torch
from huggingface_hub import hf_hub_download

# Download model files
repo_id = "antebe1/dfc-D8k-excl10-k160"
for fname in ["model.pt", "config.json", "dfc.py"]:
    hf_hub_download(repo_id=repo_id, filename=fname, local_dir="./model")

# Load the crosscoder
import sys; sys.path.insert(0, "./model")
from dfc import DFCCrossCoder

dfc = DFCCrossCoder.load("./model", device="cuda")
print(f"Loaded: dict_size={dfc.dict_size}, k={dfc.k}")

Extract Features from Real Models

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load both models
model_a = AutoModelForCausalLM.from_pretrained("chengq9/ToolRL-Qwen2.5-3B", device_map="cuda:0")
model_b = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B", device_map="cuda:1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B")

# Get activations from layer 13
# NOTE: hidden_states[0] = embeddings, hidden_states[i] = output of layer i-1
#       so layer 13 activations are at index 13+1
text = "Use the search tool to find recent papers on RLHF"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    out_a = model_a(**inputs.to("cuda:0"), output_hidden_states=True)
    out_b = model_b(**inputs.to("cuda:1"), output_hidden_states=True)
    act_a = out_a.hidden_states[13 + 1][:, -1, :]  # last token, layer 13
    act_b = out_b.hidden_states[13 + 1][:, -1, :]

# Stack and encode
activations = torch.stack([act_a.cpu(), act_b.cpu()], dim=1)  # (1, 2, 2048)
features = dfc.encode(activations.to(dfc.W_enc.device))

print(f"Active features: {(features > 0).sum().item()} / {dfc.dict_size}")

Analyze Partitions (DFC only)

stats = dfc.feature_stats(features)
print(f"L0 total:    {stats['l0_total']:.1f}")
print(f"L0 A-excl:   {stats['l0_a_excl']:.1f}")
print(f"L0 B-excl:   {stats['l0_b_excl']:.1f}")
print(f"L0 shared:   {stats['l0_shared']:.1f}")

# Check reconstruction quality
recon, feats = dfc(activations.to(dfc.W_enc.device))
mse = torch.nn.functional.mse_loss(recon.cpu(), activations)
print(f"Reconstruction MSE: {mse.item():.6f}")

Files

Part of a Sweep

This model is one of 48 models in a hyperparameter sweep. See the full collection:

Citation

@misc{dfc-D8k-excl10-k160,
  title={DFC CrossCoder: ToolRL vs Base Qwen2.5-3B},
  author={Andre Shportko},
  year={2026},
  url={https://huggingface.co/antebe1/dfc-D8k-excl10-k160}
}