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CN11-FIELD_OCI100
modrill/CN11-FIELD_OCI100
CN11-FIELD_OCI100 is a text generation model from modrill. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
DIAGNOSTICONLY / NOTWINNER / OPERATIONALSCREENINGONLY
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35
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Repo size
540 MB
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.safetensors529 MB · 98%
At a glance
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026
- SHA
- 0effe1a3
Try a prompt
Base models
Model card
From the Hugging Face model README
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 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
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
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026