Instructions to use lkjiop8/Yuanl-27B-v59-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lkjiop8/Yuanl-27B-v59-long with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/merged_v6_1_base") model = PeftModel.from_pretrained(base_model, "lkjiop8/Yuanl-27B-v59-long") - Notebooks
- Google Colab
- Kaggle
Yuanl-27B v59-long (LoRA adapter)
LoRA adapter (r=64, alpha=128) in the Yuanl-27B cybersecurity/agentic lineage.
- Architecture base:
Qwen3.6-27B(qwen3_5, with native MTPnextnheads in the full upstream checkpoint). - Lineage: trained on the SFT-merged base of the v5-x → v5-9-sft chain
(
merged_v6_1_base), then long-context (128K) continue-train. Held-out eval 7/12; with a task-completion-discipline system prompt it reaches 9/12. - Targets:
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj(language-model layers only; never touchesnextn.*/visual.*).
Note: this adapter's true training base is the private SFT-merged
merged_v6_1_base, not rawunsloth/Qwen3.6-27B. Thebase_modelfield above points at the public architecture base for reference; applying this adapter directly on the raw base reproduces only the final delta, not the full v5-x SFT chain.
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