--- title: Qwen3.8-27B-Distillation-40K canonical_url: "https://www.modelscope.ai/datasets/faunix/Qwen3.8-27B-Distillation-40K" md_url: "https://www.modelscope.ai/datasets/faunix/Qwen3.8-27B-Distillation-40K.md" repository: faunix/Qwen3.8-27B-Distillation-40K last_updated: 2026-09-17 license: apache-2.0 storage_size: "307 MB" downloads: 120 stars: 2 --- # Qwen3.8-27B-Distillation-40K > Qwen3.8-27B-Distillation-40K - An open-source dataset by faunix on ModelScope. Qwen3.8-27B-Distillation (40K Traces) faunix/Qwen3.8-27B-Distillation-40K is a dataset on ModelScope. totalling 307 MB. licensed under apache-2.0. - **Repository**: faunix/Qwen3.8-27B-Distillation-40K - **License**: apache-2.0 - **Storage size**: 307 MB - **Downloads**: 120 - **Stars**: 2 - **Last updated**: 2026-09-17 Source: https://www.modelscope.ai/datasets/faunix/Qwen3.8-27B-Distillation-40K --- ![Qwen3.8-27B](qwen3.8-27b.png) # Qwen3.8-27B-Distillation (40K Traces) Qwen3.8-27B-Distillation is a dataset containing 40,000 reasoning traces distilled from Qwen's latest model — Qwen3.8-27B. We generated this dataset locally by running the model on our own infrastructure. It covers 4 domains with prompts sourced from 12 diverse open-source datasets. # Dataset Overview | Metric | Value | |--------|-------| | Total Examples | 40,000 | | Teacher Model | Qwen3.8-27B | | Model Precision | FP8 | | Reasoning Effort | medium | | Total Tokens | 229,980,687 (~230M) | | Assistant Tokens | 224,414,445 (~224.4M) | | Thinking Tokens | 147,744,072 (~147.7M) | | User Tokens | 5,566,242 (~5.57M) | | Avg Tokens per Example | ~5,749 | | Inference Engine | vLLM | | Estimated Generation Cost | ~$80 | > **Note:** The dataset underwent basic deduplication: removal of duplicate and invalid examples. ## Domains | Domain | Examples | Percentage | |--------|----------|------------| | code | 16,006 | 40.02% | | math | 10,994 | 27.48% | | science | 7,000 | 17.50% | | logic | 6,000 | 15.00% | ## Sources Prompts were sampled from 12 open-source datasets: | Source | Examples | Percentage | |--------|----------|------------| | [ianncity/Hunter-Alpha-Programming-160000x](https://huggingface.co/datasets/ianncity/Hunter-Alpha-Programming-160000x) | 10,014 | 25.04% | | [ianncity/GLM-5.2-Logic-Puzzles](https://huggingface.co/datasets/ianncity/GLM-5.2-Logic-Puzzles) | 5,500 | 13.75% | | [ianncity/GLM-5.2-Science](https://huggingface.co/datasets/ianncity/GLM-5.2-Science) | 5,051 | 12.63% | | [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | 4,000 | 10.00% | | [nvidia/OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | 3,994 | 9.98% | | [Modotte/CodeX-2M-Thinking](https://huggingface.co/datasets/Modotte/CodeX-2M-Thinking) | 2,999 | 7.50% | | [GetSoloTech/Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning) | 2,993 | 7.48% | | [AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | 2,000 | 5.00% | | [EricLu/SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K) | 1,400 | 3.50% | | [zwhe99/DeepMath-103K](https://huggingface.co/datasets/zwhe99/DeepMath-103K) | 1,000 | 2.50% | | [Hothan/OlympiadBench](https://huggingface.co/datasets/Hothan/OlympiadBench) | 549 | 1.37% | | [lukaemon/bbh](https://huggingface.co/datasets/lukaemon/bbh) | 500 | 1.25% | | **Total** | **40,000** | **100%** | ## Structure Each example is a single JSONL line with the following schema: ```json { "messages": [ {"role": "user", "content": ""}, {"role": "assistant", "content": "...\n\n"} ], "domain": "code | math | science | logic", "category": "python | physics | logic_puzzle | ...", "source": "ianncity/Hunter-Alpha-Programming-160000x | ...", "tokens_total": 5749, "tokens_think": 3200 } ``` | Field | Type | Description | |-------|------|-------------| | `messages` | list[dict] | Standard chat format with `user` and `assistant` roles | | `messages[1].content` | str | Always wrapped in `...` tags followed by the final response | | `domain` | str | One of: `code`, `math`, `science`, `logic` | | `category` | str | Fine-grained topic (e.g. `python`, `physics`, `olympiads`) or `-` if unspecified | | `source` | str | Original dataset the prompt was sampled from | | `tokens_total` | int | Total token count (user + assistant), counted with Qwen3.8 tokenizer | | `tokens_think` | int | Token count inside `` block only | --- ## Usage ### With Hugging Face Datasets ```python from datasets import load_dataset dataset = load_dataset("faunix/Qwen3.8-27B-Distillation-40K", split="train") print(dataset[0]["messages"]) # full chat with + response print(dataset[0]["domain"]) # code / math / science / logic print(dataset[0]["tokens_think"]) # thinking tokens count ``` ### With Axolotl ```yaml datasets: - path: faunix/Qwen3.8-27B-Distillation-40K type: chat_template chat_template: qwen3_5 split: train ``` ### With Unsloth ```python from unsloth import FastLanguageModel from datasets import load_dataset model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/Qwen3.5-9B", max_seq_length=8192, ) dataset = load_dataset("faunix/Qwen3.8-27B-Distillation-40K", split="train") ``` --- # Sponsor This dataset would not exist without the help of [lium.io](https://lium.io)! By granting us GPU access, we were able to create this dataset, and we will create more, and train many more interesting models! Therefore, as a sign of gratitude, we mention them here! :) --- ## We are on... Follow us on X: [![Follow on X](https://img.shields.io/badge/Follow-Faunix__AI-000000?style=for-the-badge&logo=x&logoColor=white)](https://x.com/Faunix_AI) And also subscribe to our HF organization! This way you won't miss new models and other projects we will be publishing! :) --- # ☕ Support Faunix :) We are two AI developers: [**Didiblud**](https://huggingface.co/Didiblud) (12 years old) and [**Limen4ik**](https://huggingface.co/Limen4ik) (13 years old). Your support is very important to us — just like everyone else, we want to eat, so we are leaving our donation links below! - 💳 **Card / Apple Pay / PayPal:** ⭐ **Recommended!** — [app.lava.top/faunix_ai](https://app.lava.top/faunix_ai?tabId=donate) - 💎 **TON:** `UQArgd4zYOA0I5QSK7ylSlvgyaE5Md8pm-O-RyvSnTTk8Me0` - 💵 **USDT (TRC-20):** `TVCgvfbrUQC1nKD6cHcSP7JGirEEx5f4Te` *(Please note: 60% goes into our personal pockets fund, and 40% goes directly to Faunix development — buying domains, GPU hours, etc.)* --- ## Note This dataset is open-source and distributed under the **Apache 2.0 license**, so you can use it in experiments and fine-tuning of your own models! **We look forward to seeing what models you can fine-tune based on our dataset! :)** --- # Creator: Faunix # Release Date: 22.08.26 # Dataset Name: Qwen3.8-27B-Distillation-40K # :)