Instructions to use laion/jtd-d2-20-30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laion/jtd-d2-20-30B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laion/jtd-d2-20-30B") model = AutoModelForCausalLM.from_pretrained("laion/jtd-d2-20-30B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
laion/jtd-d2-20-30B
Jupiter TaskTrove DAPO campaign, arm D2 — full-DAPO arm — D1 + DAPO §3.4 overlong shaping (per-trajectory, l_max 12288 / l_cache 3072), filter on unshaped rewards.
Dataset: laion/codeforces-v2 (10,000 competitive-programming tasks; ckpt_interval 1).
Base model Qwen/Qwen3-Coder-30B-A3B-Instruct; FSDP2 fully-async trainer, 16 policy GPUs
(4 nodes x 4 GH200) + 4 vLLM engines (TP2); Harbor/Daytona sandboxed terminus-2 rollouts.
- Checkpoint: step 20 of 80, selected by trailing-5 EMA of
reward/avg_raw_rewardover the arm's full step series (campaign ended early on platform degradation + data-quality grounds; final banked step 20). - Launch config:
rl_config.yamlin this repo; metrics + per-link logs intraining_logs/. - pass@8 reads 1.0 on the dynamic-sampling arms (d1/d2/d4) are post-filter by construction;
use
reward/avg_raw_rewardseries intraining_logs/for cross-arm comparison.
Training Traces
Training-time Daytona/Harbor rollouts: penfever/jtd-d2
— the last episode of each trial, conserved at a 25% subsample: every 4th trial dir of
the arm's full trace store (28495 enumerated at upload; 143 shards, 25433 rows).
Owner-instructed conservation quota for this campaign's cleanup, not the uploader default.
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Model tree for laion/jtd-d2-20-30B
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct