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

File size: 546 Bytes

5ab0961

{
  "format_version": 1,
  "scheme": "temperature",
  "model_id": "e1e-qwen3.5-2b-holdout",
  "calibration_version": 1,
  "default_temperature": 3.361557,
  "temperatures": {
    "choice": 3.361557
  },
  "fit": {
    "items": 120,
    "ece_before": 0.179237,
    "ece_after": 0.116711,
    "source": "e1 slot-readout eval of /home/mkinney/t5500-bundle/eval/suite_holdout.json (sha256 6e9b6e0fe3ded2bb), single-run arm eval-holdout-best, model /home/mkinney/t5500-bundle/runs/e1-e-qwen2b/adapter-best; winner-vs-rest margin fit, in-sample"
  }
}