CN11-FIELD_OCI100 / README.md
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---
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