Scaling Data Environments from Scratch
Specify the world behind the files. Generate the data. Ground the supervision.
DataWorld-35B-A3B
A reinforcement-learning fine-tune of
Qwen/Qwen3.6-35B-A3B
(35B total / 3B activated, Apache-2.0), trained with multi-turn agentic RL on the
DataWorld task suite through a mini-swe-agent harness. This repository holds the
resulting checkpoint, exported to Hugging Face safetensors format.
Format
A standard transformers checkpoint — weights only, no optimizer state and no
training configuration. The layout is the base model's:
| architecture | Qwen3_5MoeForConditionalGeneration (model_type: qwen3_5_moe), bfloat16 |
| parameters | 35B total / 3B activated — 40 layers, 256 experts, top-8 routed |
| size on disk | 70.21 GB across 14 safetensors shards (31,666 tensors) |
| hidden size | 2048 (text) |
| vocabulary | 248,320 tokens |
| context | 262,144 tokens (max_position_embeddings) |
| attention | hybrid: 3 × linear_attention + 1 × full_attention per block |
| base model | Qwen/Qwen3.6-35B-A3B |
Serving
The architecture, config and weight layout are the base model's, so the official Qwen3.6 deployment recipe applies unchanged — only the model path changes.
python -m sglang.launch_server \
--model-path Zhanli-Li/DataWorld-35B-A3B \
--port 8000 --tp-size 8 \
--mem-fraction-static 0.8 --context-length 262144 \
--reasoning-parser qwen3 --tool-call-parser qwen3_coder
- Requires
sglang >= 0.5.10,vllm >= 0.19.0, or atransformersthat knowsqwen3_5_moe. Theqwen3reasoning parser andqwen3_codertool-call parser are what the official card prescribes, and this checkpoint was trained on tool-calling agent trajectories, so keep them enabled. - The vision tower is included, the MTP block is not. The
model.visual.*tensors ship in their own shard, so multimodal serving works as on the base model;mtp.*is absent, so the base card's speculative-decoding recipes do not apply here. - The long-context (YaRN) overrides apply as they are:
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1/VLLM_ALLOW_LONG_MAX_MODEL_LEN=1with--context-length/--max-model-lenraised to 1,010,000.
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "Zhanli-Li/DataWorld-35B-A3B" # or a local path
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(repo)
Links
- Project page — https://zhanli-li.github.io/Data-World/
- Paper — https://arxiv.org/abs/2610.21656
- Code, data generation pipeline and evaluation harness — https://github.com/Zhanli-Li/Data-World
- Benchmark and training data — https://huggingface.co/collections/Zhanli-Li/dataworld
License
Both the base model and this fine-tune are released under the Apache License 2.0 —
see LICENSE and the
base model card.