Instructions to use cds-jb/qwen3-8b-parallel-cot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cds-jb/qwen3-8b-parallel-cot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "cds-jb/qwen3-8b-parallel-cot") - Notebooks
- Google Colab
- Kaggle
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c9629a1 74fd26a c9629a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # training_code — single-token-per-step latent-CoT organism
- `latent_threads/single.py` — single-token Markov mask, the T-step in-graph forward with codebook
feedback, readouts (one z_t per step; contrast latent_threads/markov.py).
- `latent_threads/train_single.py` — trainer (answer CE + per-cell feedback CE, teacher-forcing
anneal, mixed chain lengths for length generalisation, separable codebook init).
- `latent_threads/eval_single_report.py` — causal load-bearing battery + length-generalisation curve.
- `latent_threads/eval_single_cellpatch.py` — single-cell `c_i(t):=d` patch + CA-propagation check.
- `latent_threads/plot_single_summary.py` — training curve (readout/state/curriculum vs TF anneal).
- `latent_threads/tasks.py` — the diffuse coupled-CA task. Deps: abstract_cot/masking.py,
model_organisms/envs/base.py.
Retrain: `python -m latent_threads.train_single --config latent_threads/configs/single_k3m6.json --batch-id sg1`.
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