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violetxi
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Model Details
Model Provider
violetxi
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
Full Qwen3.5-9B model trained on 100M notes + note-conditioned trajectory tokens, with KL coefficient 0.05. This revision is checkpoint-3068, after 2 epoch(s) and 3,068 updates.
| Revision | Checkpoint | Epoch |
|---|---|---|
main, final, checkpoint-3068 | checkpoint-3068 | 2 |
epoch1, checkpoint-1534 | checkpoint-1534 | 1 |
The dataset contains 99,998,917 supervised tokens per epoch before causal shifting: 69,999,985 note tokens and 29,998,932 assistant trajectory tokens. After shifting, each epoch has 99,993,091 supervised tokens. The full run trains for two epochs; KL replay is additional.
Objective: notes next-token prediction + assistant-masked note-conditioned trajectory next-token prediction + 0.05 × KL(base || student). The frozen reference is Qwen/Qwen3.5-9B at c202236235762e1c871ad0ccb60c8ee5ba337b9a. Replay comes from harvey-kl-ground-sessions: 2,048 sessions, context limit 8,192, up to 128 assistant prediction positions per draw. KL uses the full vocabulary and the mean of session means.
Training uses context rows of 16,384 tokens, global batch 8, gradient accumulation 1, learning rate 5e-6 with cosine schedule, warmup ratio 0.03, seed 0, and 8 A100. Slurm training job: 192149. Online W&B run.
Held-out KL at this checkpoint: 0.010288765895, over 256 sessions / 32,768 prediction positions. This measures drift from the base. Downstream benchmark evaluations have not been run for this checkpoint.
Four composite safetensors shards contain the trained text weights converted to the pinned base serving dtype, with the base auxiliary components retained. Config, tokenizer, chat template, processors, and generation settings are included. No adapter or local reconstruction is required.
python
from transformers import AutoModelForImageTextToText, AutoTokenizermodel_id = "violetxi/qwen35-9b-harvey-v4-notes-conditioned-100m-kl-0p05"revision = "main" # main: final; epoch1: first epochtokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)model = AutoModelForImageTextToText.from_pretrained( model_id, revision=revision, dtype="auto", device_map="auto")