# DeepSeek real-weight execution probes

The probes in this directory use official DeepSeek artifacts and keep their
scope deliberately narrower than a full-model benchmark.

## DeepSeek-V2-Lite truncated trace

`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:

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

Required Python stack:

```text
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:

```bash
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:

```bash
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
```