mempool Qwen Logits Orchestrator
The latest intended coordinator backbone for this repository is
Qwen/Qwen3-0.6B, the current official Qwen 0.6B text-generation model.
The repository stores routing-head artifacts for the mempool Qwen-small
logits-head orchestrator path with a deterministic held-out gate. The checkpoint
stores only trained routing heads, not Qwen base model weights. A refreshed
checkpoint should be trained and uploaded before using these heads with the
latest Qwen 0.6B backbone.
- Checkpoint:
qwen_logits_heads.pt - Training rows:
53 - Final training loss:
2.1550024236951555
Worker labels:
ollama-cloud-deepseek-v4-proollama-cloud-glm-5.2ollama-cloud-kimi-k2.7-codeollama-cloud-qwen3-coder-480b
Train-row evaluation:
- Worker accuracy:
0.7358490566037735 - Workflow accuracy:
0.9433962264150944 - Mean worker loss:
1.124755915605797 - Mean workflow loss:
0.142171498247475
Held-out evaluation:
- Worker accuracy:
0.5384615384615384 - Workflow accuracy:
0.7692307692307693 - Mean worker loss:
1.3366980185875525 - Mean workflow loss:
0.672384113073349
A sample router prediction is included at sample_prediction.json.
This is a v0 research artifact. It is usable for routing experiments, but it is not yet a promoted production policy.
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# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blazeofchi/mempool-qwen-logits-orchestrator-v0", dtype="auto")