GwendalTsang/mistral-7b-hidden-states-tpu-verification

Mistral-7B TPU hidden-state extractor verification

Verification-only artifact — not a reproduction of the paper's AUROC.

This dataset contains last-token embeddings and hidden states extracted from 1,000 flattened CoQA validation question/reference-answer pairs with mistralai/Mistral-7B-Instruct-v0.3. It verifies that the memory-bounded TPU v5e-1 extraction path runs successfully. It does not contain the Mistral best_answer generations or hallucination labels required to reproduce Table 2 of Automatic Layer Selection for Hallucination Detection.

The paper authors' public repository does not contain its prepared_data records. Do not treat this artifact as evidence for or against the paper's FEPoID or hidden-state-probing AUROC claims.

Tensor layout

Each states/validation/shard-*.safetensors file contains BF16 tensors:

Key	Shape	Meaning
embedding	[N, 4096]	Last-position input-token embedding
hidden_states	[N, 32, 4096]	Last-position state for every Mistral layer

Layers 0–30 are post-transformer-block states. Layer 31 is post-final-RMSNorm, matching the tensors selected as outputs.hidden_states[1:] by the released CUDA code. metadata-validation.jsonl maps each source index to a shard and offset. inputs-validation.jsonl contains the exact prompt-answer text and its SHA-256 digest.

Memory-bounded TPU method

• Loads MistralModel, omitting the unused language-model head.
• Uses BF16, use_cache=False, XLA_NO_SPECIAL_SCALARS=1, and PyTorch/XLA JIT.
• Never enables output_hidden_states=True.
• Hooks only [:, -1, :] after each layer and performs one host transfer per batch.
• Uses static sequence buckets of 128, 256, 512, 1,024, and 2,048 tokens.
• Saves resumable SafeTensors shards rather than accumulating the dataset in RAM.

The 1,000-record extraction completed in 215.8 seconds after model placement, using the 256-, 512-, and 1,024-token buckets without truncating any record.

Loading

import json
from huggingface_hub import hf_hub_download
from safetensors import safe_open

repo_id = "GwendalTsang/mistral-7b-hidden-states-tpu-verification"
metadata_path = hf_hub_download(
    repo_id, "metadata-validation.jsonl", repo_type="dataset"
)
row = json.loads(open(metadata_path, encoding="utf-8").readline())
shard_path = hf_hub_download(repo_id, row["shard"], repo_type="dataset")
with safe_open(shard_path, framework="pt", device="cpu") as shard:
    embedding = shard.get_tensor("embedding")[row["offset"]]
    hidden_states = shard.get_tensor("hidden_states")[row["offset"]]

See scripts/extract_mistral_hidden_states_tpu.py for reproduction and for processing paper-compatible JSONL records containing context, question, best_answer, and label. The extractor supports both --answer-view full and the authors' rule-based --answer-view first_sentence.

Provenance

• Model revision: c170c708c41dac9275d15a8fff4eca08d52bab71
• CoQA revision: 0d9e9952f1ef6e5415492d3d84b5873259137e3c
• Paper code revision: def3cb6d262c11d252e6c5a6b7e04375b94b54db
• Paper: https://arxiv.org/abs/2605.26366
• Released code: https://github.com/DesoloYw/Automatic-Layer-Selection-for-Hallucination-Detection