Instructions to use veriloop-lab/veriloop-coder-e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use veriloop-lab/veriloop-coder-e1 with Transformers:

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

pipe = pipeline("text-generation", model="veriloop-lab/veriloop-coder-e1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("veriloop-lab/veriloop-coder-e1")
model = AutoModelForImageTextToText.from_pretrained("veriloop-lab/veriloop-coder-e1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use veriloop-lab/veriloop-coder-e1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use veriloop-lab/veriloop-coder-e1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "veriloop-lab/veriloop-coder-e1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "veriloop-lab/veriloop-coder-e1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/veriloop-lab/veriloop-coder-e1

SGLang

How to use veriloop-lab/veriloop-coder-e1 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 "veriloop-lab/veriloop-coder-e1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "veriloop-lab/veriloop-coder-e1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "veriloop-lab/veriloop-coder-e1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "veriloop-lab/veriloop-coder-e1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use veriloop-lab/veriloop-coder-e1 with Docker Model Runner:
```
docker model run hf.co/veriloop-lab/veriloop-coder-e1
```

veriloop-coder-e1

File size: 1,675 Bytes

ddc62db

{
  "adapter_name": "rollback_adapter",
  "base_model": "VeriLoop Coder-E1 27B backbone bundled in the repository root",
  "created_at_unix": 1778220322,
  "display_name": "VeriLoop Coder-E1 Rollback Adapter",
  "head_files": [
    "rollback_head.pt"
  ],
  "included_files": [
    "README.md",
    "adapter/README.md",
    "adapter/adapter_config.json",
    "adapter/adapter_model.safetensors",
    "metrics_summary.json",
    "rollback_head.pt",
    "veriloop_adapter_manifest.json"
  ],
  "logs_excluded": true,
  "private_paths_sanitized": true,
  "quality_summary": {
    "eval_metrics": {
      "best_quality_score": 0.7339887002259493,
      "mean_mae": null,
      "mean_rmse": null,
      "moderate_accuracy": null,
      "quality_gate_failures": null,
      "quality_gate_passed": null,
      "quality_score": 0.7339887002259493,
      "weighted_mae": null,
      "weighted_rmse": null
    },
    "status": "trained",
    "train_metrics": {
      "adapter_exported": true,
      "best_epoch": 3,
      "best_quality_score": 0.7339887002259493,
      "peft_method": "lora_narrow",
      "used_peft": true
    },
    "version": "veriloop.rollback_adapter_trainer.v10.11.qwen36_27b_dense_stable_semantic_decoder"
  },
  "raw_internal_manifest_uploaded": false,
  "raw_plan_uploaded": false,
  "raw_training_result_uploaded": false,
  "repo_id": "veriloop-lab/veriloop-coder-e1",
  "role": "Rollback/revision obedience, validator-aware repair boundaries, worktree-safe correction behavior.",
  "schema": "veriloop.public_adapter_manifest.v1",
  "source_run_tag": "rollback_qwen36_rootfix_run1",
  "tokenizer_snapshot_excluded": true,
  "training_data_excluded": true
}