Instructions to use modrill/CN11-FIELD_OCI100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use modrill/CN11-FIELD_OCI100 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Base") model = PeftModel.from_pretrained(base_model, "modrill/CN11-FIELD_OCI100") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-4B-Base | |
| library_name: peft | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| tags: | |
| - lora | |
| - peft | |
| - qwen3 | |
| - code | |
| - livecodebench | |
| - diagnostic-only | |
| # CN11 FIELD_OCI100 LoRA (Qwen3-4B-Base) | |
| **DIAGNOSTIC_ONLY / NOT_WINNER / OPERATIONAL_SCREENING_ONLY** | |
| This is a **PEFT LoRA adapter only** (not merged full weights) for | |
| [`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base) revision `906bfd4b4dc7f14ee4320094d8b41684abff8539`. | |
| User shorthand **CI100** maps to the local CN11 arm **`FIELD_OCI100`**. | |
| There is no literal `CI100` / `CN11_CI100` adapter in the local experiment tree. | |
| ## Identity | |
| | Field | Value | | |
| | --- | --- | | |
| | Local arm | `FIELD_OCI100` | | |
| | Route | `fieldfix_targeted` (`run_20260815T072135Z`) | | |
| | Train seed | `43` | | |
| | Train status | `COMPLETE` (attempt 3) | | |
| | Update mode | `token_balanced_64` (U=64) | | |
| | Dose | 500,000 non-padding unique clean supervised target tokens | | |
| | Recipe | Nemotron CP-v2 final-code **400K** + OCI fieldfix_v2 **100K** | | |
| | Rows | 1,832 (1,298 CP-v2 + 534 OCI fieldfix); reasoning tokens = 0 | | |
| | Cutoff | 2048 | | |
| | LoRA | r=64, alpha=128, dropout=0.0, target-only CE, AdamW 1e-5, cosine+warmup | | |
| | Local adapter | `.../training/FIELD_OCI100/seed_43/attempt3/adapter` | | |
| | `adapter_manifest_sha256` | `927727c4434afcca19179a7fdcf8266bfbd79e848e062190bc27e6734ea47f40` | | |
| ## Public full-LCB diagnostic (1055 tasks, seeds 5227 / 5233 / 5303) | |
| Source: local `PUBLIC_DIAGNOSTIC_AGGREGATE.json` for the fieldfix targeted route. | |
| **Not** a hidden-dev confirmation and **not** a winner gate. | |
| | Entity | 3-seed mean pass@1 | | |
| | --- | --- | | |
| | `FIELD_OCI100` | **24.83%** | | |
| | `BASE` | 24.39% | | |
| | `FIELD_FC500_CONTROL` | 23.76% | | |
| Paired task-cluster bootstrap (10,000 replicates): | |
| - vs BASE: **+0.44pp**, 95% CI **[-0.92, +1.77] pp — crosses zero** | |
| - vs FC500 control: **+1.07pp**, 95% CI **[+0.16, +1.99] pp — excludes zero** | |
| Per-seed pass@1: 5227=25.21%, 5233=24.74%, 5303=24.55%. | |
| ## Load with PEFT | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| base_id = "Qwen/Qwen3-4B-Base" | |
| base_rev = "906bfd4b4dc7f14ee4320094d8b41684abff8539" | |
| adapter_id = "modrill/CN11-FIELD_OCI100" | |
| tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_rev, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base_id, | |
| revision=base_rev, | |
| torch_dtype="bfloat16", | |
| device_map="auto", | |
| trust_remote_code=True, | |
| ) | |
| model = PeftModel.from_pretrained(model, adapter_id) | |
| model.eval() | |
| ``` | |
| Do **not** treat this as a merged standalone model. The published `adapter_config.json` | |
| rewrites the training-time local `base_model_name_or_path` to `Qwen/Qwen3-4B-Base`; | |
| weights (`adapter_model.safetensors`) are byte-identical to the COMPLETE local adapter. | |
| ## What this is not | |
| - Not a winner / not confirmation / not holdout | |
| - Not D2 / mix / THINK | |
| - Not a full-weight merge | |
| - Positive signal vs FC500 is a **final-code mix** result, not a long-CoT / think contract | |