| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - speculative-decoding |
| - distillation |
| - kimi-k3 |
| - off-policy |
| - atom |
| - code |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # ATOM regen — OpenCodeInstruct, answered by Kimi-K3 |
|
|
| Prompts from [`nvidia/OpenCodeInstruct`](https://huggingface.co/datasets/nvidia/OpenCodeInstruct) |
| with **every answer regenerated by Kimi-K3 itself**, at a **32768** window, for |
| off-policy speculative-decoding distillation. |
|
|
| **227,329 rows**, single-turn. |
|
|
| ## Composition |
|
|
| Single source, single `source` value (`oci`). Selection: |
|
|
| | step | rows | |
| | --- | --- | |
| | rendered prompts, upstream | 4,825,044 | |
| | of those, tier-0 (upstream unit tests all pass) | 1,553,642 | |
| | sampled | 1,000,000 | |
| | generated and verified | 244,695 | |
| | **kept** | **227,329** | |
|
|
| **Only tier-0 was used.** Rows whose upstream `average_test_score` is perfect are |
| the low-entropy, template-shaped workload a speculator accepts best, which is |
| what this data is for. |
|
|
| | | mean | p50 | p90 | p99 | max | |
| | --- | --- | --- | --- | --- | --- | |
| | prompt tokens | 303.4 | 285 | 416 | 601 | 1,702 | |
| | answer tokens | 3,231.5 | 1,450 | 8,233 | 24,452 | 32,461 | |
|
|
| 734,612,605 answer tokens in total. 9.8% of rows exceed 8192 tokens, 3.5% exceed |
| 16384. |
|
|
| ## Why code |
|
|
| In our own corpus, code deduped down to 0.43% of the mix and SWE-bench Pro was |
| the second-weakest benchmark at 84.9% of the reference draft. Raising code to 20% |
| — mostly with this data — took it to 100.6%, the largest relative gain of the |
| thirteen benchmarks we track. |
|
|
| Short prompts with long answers also make this a cheap source of long-context |
| positions compared with regenerating long prompts. |
|
|
| ## Format |
|
|
| ```json |
| {"conversations": [ |
| {"role": "user", "content": "..."}, |
| {"role": "assistant", "reasoning_content": "...", "content": "..."} |
| ], "source": "oci"} |
| ``` |
|
|
| `reasoning_content` holds Kimi-K3's thinking channel, stored separately from |
| `content`. Multi-turn rows keep the full assistant reasoning of earlier turns: |
| K3 is trained that way, and stripping historical thinking leaves empty think |
| blocks that do not match the context seen at training time. |
|
|
|
|
| ## Generation |
|
|
| | | | |
| | --- | --- | |
| | Teacher | [`moonshotai/Kimi-K3`](https://huggingface.co/moonshotai/Kimi-K3), revision `a590ce090cb049c93a33dfe8c208ec652aa20503` | |
| | Engine | **ATOM `0.1.6rc1.dev275+g83e71001e`** (commit `83e71001e94602ee8e1d04581809bded49c3f08b`) | |
| | Image | `rocm/atom-dev:nightly_202608141640` | |
| | Image digest | `sha256:2f8bd4206ad15d014ae48115eae1ee9f1db83781848a8542de7177cfbd4ac914` | |
| | torch / HIP | `2.13.0+rocm7.14.0` / `7.14.60850` | |
| | Hardware | 8 x AMD Instinct MI355X per node, `tensor_parallel_size=8` | |
| | Sampling | greedy (`temperature=0`), one sample per prompt | |
| | KV cache | fp8 (matches the serving stack this data is distilled for) | |
| | Speculative decoding | off | |
|
|
| ## Verification |
|
|
| Every row was re-rendered and re-tokenized, then compared token-for-token |
| against what the engine actually consumed and produced. Non-matching rows were |
| dropped (**7.10%**), led by unclosed thinking blocks. |
|
|
| ## Limitations |
|
|
| - Single-turn only. |
| - tier-0 only, so the distribution skews template-shaped. Harder workloads would |
| need tier-1. |
| - Prompts are short (p50 285 tokens); the long-context value is in the answers, |
| not the prompts. |
|
|
