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-smoke / 20260628-qwen-small-logits-orchestrator-split-smoke-plan.json
| { | |
| "can_train_here": true, | |
| "config": { | |
| "backend": "transformers", | |
| "base_model": "Qwen/Qwen2.5-0.5B-Instruct", | |
| "batch_size": 1, | |
| "epochs": 1, | |
| "learning_rate": 0.0002, | |
| "lora_rank": 0, | |
| "max_length": 512, | |
| "seed": 7, | |
| "train_backbone": false | |
| }, | |
| "dependency_status": { | |
| "mlx": false, | |
| "mlx_lm": false, | |
| "torch": true, | |
| "transformers": true | |
| }, | |
| "prepared_rows": "research/datasets/20260628-qwen-small-logits-orchestrator-split-train.jsonl", | |
| "record_count": 53, | |
| "rows_output": "research/datasets/20260628-qwen-small-logits-orchestrator-split-train.jsonl", | |
| "schema_version": "mempool.qwen_logits_orchestrator_plan.v1", | |
| "training_order": [ | |
| "freeze Qwen-small backbone", | |
| "train worker/workflow/verifier/abstain heads on measured soft targets", | |
| "compare held-out routing against the linear multi-head baseline", | |
| "only enable LoRA/backbone updates after the heads beat the baseline" | |
| ], | |
| "worker_ids": [ | |
| "ollama-cloud-deepseek-v4-pro", | |
| "ollama-cloud-glm-5.2", | |
| "ollama-cloud-kimi-k2.7-code", | |
| "ollama-cloud-qwen3-coder-480b" | |
| ], | |
| "workflow_labels": [ | |
| "direct", | |
| "verify_then_fallback" | |
| ] | |
| } | |