Instructions to use TaskWizerAI/opencodifier-e1e-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TaskWizerAI/opencodifier-e1e-2b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/mkinney/t5500-bundle/models/Qwen3.5-2B")
model = PeftModel.from_pretrained(base_model, "TaskWizerAI/opencodifier-e1e-2b")

Transformers

How to use TaskWizerAI/opencodifier-e1e-2b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TaskWizerAI/opencodifier-e1e-2b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TaskWizerAI/opencodifier-e1e-2b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TaskWizerAI/opencodifier-e1e-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TaskWizerAI/opencodifier-e1e-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaskWizerAI/opencodifier-e1e-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TaskWizerAI/opencodifier-e1e-2b

SGLang

How to use TaskWizerAI/opencodifier-e1e-2b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TaskWizerAI/opencodifier-e1e-2b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaskWizerAI/opencodifier-e1e-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TaskWizerAI/opencodifier-e1e-2b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaskWizerAI/opencodifier-e1e-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TaskWizerAI/opencodifier-e1e-2b with Docker Model Runner:
```
docker model run hf.co/TaskWizerAI/opencodifier-e1e-2b
```

opencodifier-e1e-2b / README.md

aliasfox

Initial upload: E1-E 2B verdict-slot adapter (opencodifier-e1e-2b)

5ab0961 verified 15 days ago

preview code

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metadata

base_model: Qwen3.5-2B
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:Qwen3.5-2B
  - lora
  - verdict-slot
  - opencodifier
  - decision-model
inference: false

OpenCodifier E1-E 2B Decision Model

A verdict-slot LoRA adapter on Qwen3.5-2B for typed decision-making (choice, boolean, score). Part of the OpenCodifier decision runtime: opencodifier.pages.dev.

Model Details

Model type: Decision classifier — verdict-slot LoRA on Qwen3.5-2B
Base model: Qwen3.5-2B (Apache-2.0)
Adapter architecture: LoRA r=16, alpha=32, dropout=0.05
License: Apache-2.0
Developed by: TaskWizerAI
Released by: TaskWizerAI

Gates Passed

Benchmark	Result	Gate	Status
Suite holdout (231 items)	0.8083 accuracy	≥ 0.75	✅ PASS
JevBench public 231	0.6840 accuracy	≥ 0.6494	✅ PASS

Full evaluation evidence: benchmarks/decision-model/results/CALIBRATION.md and benchmarks/decision-model/results/e1e-slot-231/results.jsonl.

Training

Corpus: 187,822 decision items (e1-softlabel-v2), teacher distributions from MiMo
Teacher-gold agreement: 96.1–97.7% argmax match
Hyperparameters: lr=1e-4, batch=2, accum=8, max_len=2048, fp16, sdpa, seed=42, 1 epoch
Platform: T5500 (2× V100, 120W GPU cap)

Evaluation Results

JevBench Public 231 (231 items, authors' harness)

Metric	Value
Accuracy	0.6840
Macro	0.6840
ECE	0.127 (calibrated T=3.362)
Brier	0.332
p50 latency	~1.7 s (CPU, single decision)
Replay determinism	231/231

Suite Holdout (231 items, internal)

Metric	Value
Accuracy	0.8083

Uses

Direct Use

Use via the OpenCodifier binary:

# Engine-only (no model needed)
opencodifier decide --input request.json

# With E1-E model (requires llama-server running the decision fork)
opencodifier decide --input request.json --ladder-url http://127.0.0.1:8080

Out-of-Scope Use

Not a general-purpose language model
Not trained for prose generation
Not calibrated for non-decision classification

Calibration

Temperature fitted on the suite holdout: T = 3.362. Apply the calibration artifact from results/calibration/qwen3.5-2b-q4_k_m/fit.json before evaluating on any surface other than the holdout.

How to Cite

OpenCodifier decision runtime: https://github.com/TaskWizerAI/OpenCodifier

Remarks

Corpus withheld (per OpenCodifier model release policy)
Calibration artifacts published separately
Explicit opt-in pull only — never auto-downloaded at build time