v2_lite_trace.py executes layers 0–6 from the official BF16 checkpoint. Those seven layers are fully contained in shard 1; layer 7 is split across shards 1 and 2 and is therefore outside the default evidence boundary.

Pinned model:

deepseek-ai/DeepSeek-V2-Lite@604d5664dddd88a0433dbae533b7fe9472482de0

Required Python stack:

torch==2.11.0+cu128
transformers==4.41.2
safetensors==0.8.0

The 2024 remote code does not import under Transformers 5.5 because is_torch_fx_available was removed. The probe imports the official files as a read-only local package; it does not patch the model source.

Download the metadata, tokenizer, remote code, index, and first shard with the Hugging Face CLI, then run:

python experiments/deepseek/v2_lite_trace.py \
  --artifact-dir /path/to/deepseek-v2-lite \
  --output src/data/deepseek-v2-lite-trace.json

The result contains:

real tokenizer pieces and model-derived hidden states;
the actual [B,T,576] MLA compressed projection at each executed layer;
the expanded key/value tensors stored by the Hugging Face eager cache;
token-level top-6 routed expert IDs and weights for six MoE layers;
per-layer and per-prompt expert-load summaries;
explicit boundaries against global load, expert semantics, training traces, full-model generation, and production serving claims.

Run the probe twice and compare deterministic evidence while excluding timing:

python experiments/deepseek/compare_v2_lite_traces.py \
  --first /path/to/trace-1.json \
  --second /path/to/trace-2.json \
  --output src/data/deepseek-v2-lite-trace-repro.json

README.md Unescape Escape

DeepSeek real-weight execution probes

DeepSeek-V2-Lite truncated trace

README.md