Datasets:

GwendalTsang
/

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

pretty_name: Mistral-7B TPU hidden-state extractor verification (CoQA references)
language:
  - en
task_categories:
  - feature-extraction
tags:
  - mistral
  - hidden-states
  - tpu
  - pytorch-xla
  - coqa

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