---
license: other
tags:
  - sft
  - qwen3.8
  - kimi-k3
  - fable-5
  - nanbeige
  - coding
  - reasoning
  - tool-use
  - 4096-context
---

# Qwen3.8–Kimi–Fable Distillation

A 13,700-row curated SFT mixture of reasoning, coding, instruction, and tool-use traces from three teacher datasets.

## Quick start

```python
from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="hf://datasets/j0no12/qwen38-kimi-fable-distillation/curated.jsonl",
    split="train",
)

print(dataset[0]["source"])
print(dataset[0]["messages"])
```

## Dataset summary

| Property | Value |
|---|---:|
| Rows | 13,700 |
| Split | Training collection only |
| Format | JSON Lines |
| Context policy | At most 4,096 tokens with the Nanbeige4.2-3B tokenizer |
| Main domains | Math, code, reasoning, instruction following, tool use |
| Intended use | SFT for compact agentic models |

## Composition

| `source` | Rows | Share |
|---|---:|---:|
| `qwen3.8-max` | 10,000 | 73.0% |
| `fable-5-trace` | 2,500 | 18.2% |
| `kimi-k3-trace` | 1,200 | 8.8% |
| **Total** | **13,700** | **100%** |

Recorded domain counts are 3,912 math, 2,916 reasoning, 2,650 code, 522 instruction, and 3,700 rows without a domain label.

## Schema

| Field | Type | Description |
|---|---|---|
| `messages` | list of objects | Chat messages with `role` and `content` |
| `source` | string | One of the three source labels above |
| `domain` | string or null | Domain tag when available |
| `difficulty` | source-dependent / optional | Difficulty tag when supplied by the source |

Example access pattern:

```python
row = dataset[0]
for message in row["messages"]:
    print(f"{message['role']}: {message['content']}")
```

## Source lineage

| Source dataset | Role | Selected rows | Terms recorded by this card |
|---|---|---:|---|
| [`r0b0tlab/qwen3.8-max-distillation-50k`](https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-distillation-50k) | General reasoning, math, code, instruction | 10,000 | See the source dataset and Alibaba Cloud Model Studio terms |
| [`greghavens/fable-5-coding-and-debugging-traces`](https://huggingface.co/datasets/greghavens/fable-5-coding-and-debugging-traces) | Coding/debugging trajectories | 2,500 | CC-BY-4.0 |
| [`greghavens/kimi-k3-coding-and-debugging-traces`](https://huggingface.co/datasets/greghavens/kimi-k3-coding-and-debugging-traces) | Coding/tool-use trajectories | 1,200 | CC-BY-4.0 |

## Construction story

The curation report records 61,150 input rows: 44,796 Qwen, 12,448 Fable, and 3,906 Kimi. Quality and length filtering retained 40,689 candidate rows before source caps were applied. The final deterministic mixture contains the 13,700 rows shown above.

Recorded filtering outcomes:

| Source | Main recorded exclusions |
|---|---|
| Qwen | 7,413 low-quality scores, 711 over-length contexts, 132 refusals, 6 short assistant responses |
| Fable | 8,196 short assistant responses, 1,380 over-length contexts, 54 refusals |
| Kimi | 2,386 short assistant responses, 174 near-duplicates, 9 refusals |

The context check used the Nanbeige4.2-3B tokenizer. User messages range from 5 to 3,027 tokens (median 75), and assistant messages range from 15 to 3,625 tokens (median 259). The raw-content target was approximately 3,800 tokens to leave space for chat-template tokens inside a 4,096-token window.

## Quality checks

- Qwen rows required a quality score of at least 7.5; the prior card reports median 9.0 and mean 8.6.
- Refusals and empty/very short completions were filtered.
- Context length was checked with the target tokenizer.
- Kimi processing records a near-duplicate filter; the previous card also described SHA-256 hashing, which should be understood as exact content hashing unless a separate similarity method is supplied.
- Source caps fix the final mixture at 10,000 / 2,500 / 1,200 rows.

## Contamination screening

No benchmark-contamination report is included. Hash-based deduplication and source-specific near-duplicate filtering do not prove independence from HumanEval, MBPP, GSM8K, MATH, or other public evaluations. Run task-specific decontamination before reporting benchmark results from models trained on this collection.

## Known limitations

- All responses are teacher-generated and may contain incorrect reasoning, insecure code, fabricated tool results, or stylistic artifacts.
- There is no held-out validation or test split.
- The 4,096-token guarantee is tokenizer-specific and may not hold under another tokenizer or chat template.
- Domain labels are missing for 3,700 rows, and optional metadata is source-dependent.
- The mixture intentionally oversamples Qwen relative to the two trajectory sources.
- Component licensing is mixed; no single permissive license is asserted for every row.

## Files and integrity

| File | Size | Integrity identifier |
|---|---:|---|
| `curated.jsonl` | 64,322,634 bytes | SHA-256 `78d2d52229b49e057fad41cf3ec66a60e5be7914997b55938cb3ba863365bb8a` |
| `report.json` | 1,303 bytes | Git blob `095b1324ac986777402e3cb58a1fbfa792a67fd5` |
| Repository revision documented by this card update | — | `95156b7351b5a7689b5aa6eee037337d3ee5293f` |

## Citation

```bibtex
@misc{j0no12_qwen38_kimi_fable_2026,
  author       = {j0no12},
  title        = {Qwen3.8--Kimi--Fable Distillation},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/j0no12/qwen38-kimi-fable-distillation}}
}
```

Please also cite the three source datasets.

## License

Mixed / other. Fable and Kimi components are recorded as CC-BY-4.0; Qwen-derived traces remain subject to their source dataset and Alibaba Cloud Model Studio terms. This card does not grant rights beyond those upstream terms.