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: 2,891 Bytes

a856587

{
  "adapter_exported": true,
  "auto_lora_from_ia3": false,
  "best_epoch": 2,
  "best_quality_score": 0.8625935807221907,
  "dataset_summary": {
    "eval_modes": {
      "conflicting_evidence": 5,
      "evidence_gap": 5,
      "exec_required": 5,
      "high_risk": 5,
      "low_uncertainty": 5,
      "patch_pending": 5,
      "reverse_engineering_ambiguity": 5,
      "self_check_failure": 5,
      "spec_mismatch": 5,
      "validator_negation": 5,
      "worktree_conflict": 5
    },
    "eval_size": 55,
    "train_modes": {
      "conflicting_evidence": 14,
      "evidence_gap": 14,
      "exec_required": 14,
      "high_risk": 14,
      "low_uncertainty": 14,
      "patch_pending": 14,
      "reverse_engineering_ambiguity": 14,
      "self_check_failure": 14,
      "spec_mismatch": 14,
      "validator_negation": 14,
      "worktree_conflict": 14
    },
    "train_size": 154
  },
  "epochs_completed": 4,
  "eval_metrics": {
    "adapter_exported": true,
    "auto_lora_from_ia3": false,
    "best_epoch": 2,
    "best_quality_score": 0.8625935807221907,
    "count": 55,
    "mae": {
      "u_answer": 0.15174226462841034,
      "u_evidence": 0.19610758125782013,
      "u_exec": 0.18561214208602905,
      "u_risk": 0.1553734689950943,
      "u_spec": 0.21633382141590118
    },
    "mean_mae": 0.18103384971618652,
    "mean_rmse": 0.24169571697711945,
    "moderate_accuracy": 0.6727272727272727,
    "peft_method": "lora_narrow",
    "quality_score": 0.8625935807221907,
    "rmse": {
      "u_answer": 0.18724055588245392,
      "u_evidence": 0.22527915239334106,
      "u_exec": 0.25238174200057983,
      "u_risk": 0.20667441189289093,
      "u_spec": 0.3369026482105255
    },
    "tight_accuracy": 0.4,
    "used_peft": true,
    "weighted_mae": 0.18083095811830807,
    "weighted_rmse": 0.24125460771003793
  },
  "load_meta": {
    "chosen_class": "AutoModelForCausalLM",
    "hidden_size": 2048,
    "quantization_mode": "4bit"
  },
  "peft_method": "lora_narrow",
  "requested_method": "lora_narrow",
  "requested_target_modules": [
    "uncertainty_head",
    "uncertainty_head.calibration_mlp",
    "uncertainty_head.proj"
  ],
  "resolved_target_modules": [
    "surface_host.uncertainty_head.calibration_mlp",
    "surface_host.uncertainty_head.proj"
  ],
  "selected_target_modules": [
    "uncertainty_head",
    "uncertainty_head.calibration_mlp",
    "uncertainty_head.proj"
  ],
  "status": "trained",
  "train_metrics": {
    "adapter_exported": true,
    "auto_lora_from_ia3": false,
    "best_epoch": 2,
    "best_quality_score": 0.8625935807221907,
    "epochs_completed": 4,
    "loss": 0.009006613283418119,
    "micro_batches": 154,
    "micro_batches_total": 616,
    "optimizer_steps": 10,
    "optimizer_steps_total": 40,
    "peft_method": "lora_narrow",
    "used_peft": true
  },
  "unresolved_target_modules": [],
  "used_peft": true
}