Instructions to use blazeofchi/mempool-qwen-logits-orchestrator-smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blazeofchi/mempool-qwen-logits-orchestrator-smoke with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blazeofchi/mempool-qwen-logits-orchestrator-smoke", dtype="auto") - Notebooks
- Google Colab
- Kaggle
mempool Qwen Logits Orchestrator Split Smoke
This repository contains a split-smoke checkpoint for the mempool
Qwen-small logits-head orchestrator path with a deterministic held-out gate.
The checkpoint stores only the trained routing heads, not the Qwen base model weights. Load the base model separately and attach the heads.
- Checkpoint:
qwen_logits_heads.pt - Training rows:
53 - Final smoke loss:
4.2856650959770635
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:
0.4716981132075472 - Workflow accuracy:
0.5660377358490566 - Mean worker loss:
1.4386708387788736 - Mean workflow loss:
0.7223093554790501
Held-out evaluation:
- Worker accuracy:
0.3076923076923077 - Workflow accuracy:
0.7692307692307693 - Mean worker loss:
1.7572260361451368 - Mean workflow loss:
0.5600809453485104
This is a smoke artifact, not a promoted production policy.
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