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license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- hallucination-detection
- token-classification
- qwen3
language:
- en
---
# TokenHD-8B (multi-domain)
**TokenHD** is a token-level hallucination detector trained on top of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) using the TokenHD pipeline. It assigns a hallucination probability to each token in an LLM-generated response, enabling fine-grained localization of errors without requiring predefined step segmentation.
Paper: [arxiv.org/abs/2605.12384](https://arxiv.org/abs/2605.12384)
Code: [github.com/rmin2000/TokenHD](https://github.com/rmin2000/TokenHD)
Training Data: [mr233/TokenHD-training-data](https://huggingface.co/datasets/mr233/TokenHD-training-data)
---
## Model Details
| Property | Value |
|---|---|
| Base model | `Qwen/Qwen3-8B` |
| Architecture | `AutoModelForTokenClassification` (`num_labels=1`) |
| Training domain | Mathematics and code generation (multi-domain training) |
| Output | Per-token hallucination probability (sigmoid of logits) |
---
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
model_id = "mr233/TokenHD-8B-Mix"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id, num_labels=1)
model.eval()
problem = "What is the capital of France?"
response = "The capital of France is London."
messages = [
{"role": "user", "content": problem},
{"role": "assistant", "content": response},
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=False)[:-2]
input_tensor = torch.tensor(input_ids).unsqueeze(0)
with torch.no_grad():
logits = model(input_ids=input_tensor).logits # shape: (1, seq_len, 1)
# scores for response tokens only
response_ids = tokenizer.encode(response, add_special_tokens=False)
scores = torch.sigmoid(logits.squeeze(-1).squeeze(0))[-len(response_ids):]
# scores[i] is the hallucination probability for the i-th response token
```
---
## Evaluation
Use the [TokenHD eval dataset](https://huggingface.co/datasets/mr233/TokenHD-eval-data) to compute **S_incor** (token F1 on hallucinated samples) and **S_cor** (recall on hallucination-free samples):
```python
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
import numpy as np
def hard_f1(y_true, y_pred):
if max(y_true) == 0:
y_true, y_pred = 1 - y_true, 1 - y_pred
tp = np.sum((y_pred == 1) & (y_true == 1))
fp = np.sum((y_pred == 1) & (y_true == 0))
fn = np.sum((y_pred == 0) & (y_true == 1))
precision = tp / (tp + fp + 1e-7)
recall = tp / (tp + fn + 1e-7)
f1 = 2 * precision * recall / (precision + recall + 1e-7)
return precision, recall, f1
model_id = "mr233/TokenHD-8B-Mix"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(
model_id, num_labels=1, torch_dtype=torch.bfloat16, device_map="auto"
)
model.eval()
benchmarks = [
"tokenhd_eval_math_500",
"tokenhd_eval_math_aime",
"tokenhd_eval_math_gpqa",
"tokenhd_eval_math_fin_qa",
"tokenhd_eval_math_olym",
"tokenhd_eval_math_olym_phy",
"tokenhd_eval_code_codeelo",
"tokenhd_eval_code_live_code_lite",
]
for bench in benchmarks:
dataset = load_dataset("mr233/TokenHD-eval-data",
data_files=f"{bench}.jsonl", split="train")
f1_incor, f1_cor = [], []
for item in dataset:
token_weights_gt = np.array(item["token_weights"], dtype=np.float32)
gt_hard = (token_weights_gt > 0.5).astype(np.float32)
messages = [{"role": "user", "content": item["problem"]},
{"role": "assistant", "content": item["raw_answer"]}]
input_ids = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=False)[:-2]
input_tensor = torch.tensor(input_ids, device=model.device).unsqueeze(0)
with torch.no_grad():
logits = model(input_ids=input_tensor).logits
scores = torch.sigmoid(logits.squeeze(-1).squeeze(0))[-len(gt_hard):]
pred_hard = (scores.float().cpu().numpy() > 0.5).astype(np.float32)
_, _, f1 = hard_f1(gt_hard, pred_hard)
if item["correctness"] == -1:
f1_incor.append(f1)
else:
f1_cor.append(f1)
s_incor = np.mean(f1_incor) * 100 if f1_incor else float("nan")
s_cor = np.mean(f1_cor) * 100 if f1_cor else float("nan")
print(f"{bench:<44s} S_incor={s_incor:.2f} S_cor={s_cor:.2f}")
```
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