Text Generation
Transformers
Safetensors
PEFT
English
Chinese
qwen3_5
image-text-to-text
veriloop
veriloop-coder
code
coding-agent
software-engineering
repository-understanding
tool-use
lora
harness-engineering
evidence-binding
rollback
uncertainty-calibration
long-context
open-weights
conversational
Instructions to use veriloop-lab/veriloop-coder-e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "hidden_size": 2048, | |
| "host_config": { | |
| "attn_implementation": null, | |
| "backbone_name_or_path": "/public/wang_libo/veriloop_coder_e1/model", | |
| "device_map": null, | |
| "dtype": null, | |
| "evidence_rank_hint": 8, | |
| "expose_backbone_inventory": false, | |
| "freeze_backbone": true, | |
| "hidden_size_override": 2048, | |
| "host_dropout": 0.0, | |
| "identity_rank_hint": 8, | |
| "load_backbone_weights": false, | |
| "local_files_only": true, | |
| "low_cpu_mem_usage": true, | |
| "memory_rank_hint": 4, | |
| "rollback_rank_hint": 8, | |
| "runtime_rank_hint": 8, | |
| "toolspec_rank_hint": 8, | |
| "trust_remote_code": true, | |
| "uncertainty_rank_hint": 8, | |
| "use_safetensors": null, | |
| "validator_rank_hint": 8 | |
| }, | |
| "load_result": { | |
| "has_base_config": true, | |
| "has_base_model": true, | |
| "hidden_size": 2048, | |
| "notes": [ | |
| "class=AutoModelForCausalLM", | |
| "quant=4bit" | |
| ], | |
| "source": "trainer_qwen36_loader" | |
| }, | |
| "peft_named_modules": [ | |
| "claim_extractor", | |
| "claim_extractor.adapter", | |
| "dropout", | |
| "episodic_memory", | |
| "episodic_memory.adapter", | |
| "evidence_binding", | |
| "evidence_binding.adapter", | |
| "failure_signal_bridge", | |
| "failure_signal_bridge.rollback_bridge", | |
| "identity_adapter", | |
| "identity_adapter.bridge", | |
| "identity_guard", | |
| "identity_guard.adapter", | |
| "input_norm", | |
| "memory_boundary_guard", | |
| "memory_boundary_guard.adapter", | |
| "memory_boundary_guard.rollback_filter", | |
| "patch_binding", | |
| "patch_binding.adapter", | |
| "permission_context_manager", | |
| "permission_context_manager.adapter", | |
| "progress_state_tracker", | |
| "progress_state_tracker.adapter", | |
| "progress_state_tracker.rollback_memory", | |
| "proof_carrying_hints", | |
| "proof_carrying_hints.bridge", | |
| "provenance_binding", | |
| "provenance_binding.adapter", | |
| "public_identity_head", | |
| "public_identity_head.proj", | |
| "query_runtime_engine", | |
| "query_runtime_engine.adapter", | |
| "request_normalizer", | |
| "request_normalizer.adapter", | |
| "request_normalizer.adapter.base_layer", | |
| "request_normalizer.adapter.lora_A", | |
| "request_normalizer.adapter.lora_A.default", | |
| "request_normalizer.adapter.lora_B", | |
| "request_normalizer.adapter.lora_B.default", | |
| "request_normalizer.adapter.lora_dropout", | |
| "request_normalizer.adapter.lora_dropout.default", | |
| "request_normalizer.adapter.lora_embedding_A", | |
| "request_normalizer.adapter.lora_embedding_B", | |
| "request_normalizer.adapter.lora_magnitude_vector", | |
| "rollback_adapter", | |
| "rollback_adapter.head", | |
| "rollback_engine", | |
| "rollback_engine.adapter", | |
| "runtime_binding", | |
| "runtime_binding.adapter", | |
| "runtime_harness_adapter", | |
| "runtime_harness_adapter.bridge", | |
| "runtime_harness_adapter.bridge.base_layer", | |
| "runtime_harness_adapter.bridge.lora_A", | |
| "runtime_harness_adapter.bridge.lora_A.default", | |
| "runtime_harness_adapter.bridge.lora_B", | |
| "runtime_harness_adapter.bridge.lora_B.default", | |
| "runtime_harness_adapter.bridge.lora_dropout", | |
| "runtime_harness_adapter.bridge.lora_dropout.default", | |
| "runtime_harness_adapter.bridge.lora_embedding_A", | |
| "runtime_harness_adapter.bridge.lora_embedding_B", | |
| "runtime_harness_adapter.bridge.lora_magnitude_vector", | |
| "runtime_harness_uncertainty_bridge", | |
| "runtime_harness_uncertainty_bridge.adapter", | |
| "sandbox_rollback_bridge", | |
| "sandbox_rollback_bridge.adapter", | |
| "session_compactor", | |
| "session_compactor.adapter", | |
| "session_state_manager", | |
| "session_state_manager.adapter", | |
| "session_state_manager.rollback_state", | |
| "tool_protocol_adapter", | |
| "tool_protocol_adapter.bridge", | |
| "tool_protocol_adapter.bridge.base_layer", | |
| "tool_protocol_adapter.bridge.lora_A", | |
| "tool_protocol_adapter.bridge.lora_A.default", | |
| "tool_protocol_adapter.bridge.lora_B", | |
| "tool_protocol_adapter.bridge.lora_B.default", | |
| "tool_protocol_adapter.bridge.lora_dropout", | |
| "tool_protocol_adapter.bridge.lora_dropout.default", | |
| "tool_protocol_adapter.bridge.lora_embedding_A", | |
| "tool_protocol_adapter.bridge.lora_embedding_B", | |
| "tool_protocol_adapter.bridge.lora_magnitude_vector", | |
| "toolspec_bridge", | |
| "toolspec_bridge.adapter", | |
| "toolspec_bridge.adapter.base_layer", | |
| "toolspec_bridge.adapter.lora_A", | |
| "toolspec_bridge.adapter.lora_A.default", | |
| "toolspec_bridge.adapter.lora_B", | |
| "toolspec_bridge.adapter.lora_B.default", | |
| "toolspec_bridge.adapter.lora_dropout", | |
| "toolspec_bridge.adapter.lora_dropout.default", | |
| "toolspec_bridge.adapter.lora_embedding_A", | |
| "toolspec_bridge.adapter.lora_embedding_B", | |
| "toolspec_bridge.adapter.lora_magnitude_vector", | |
| "toolspec_head", | |
| "toolspec_head.param_schema_adapter", | |
| "toolspec_head.param_schema_adapter.base_layer", | |
| "toolspec_head.param_schema_adapter.lora_A", | |
| "toolspec_head.param_schema_adapter.lora_A.default", | |
| "toolspec_head.param_schema_adapter.lora_B", | |
| "toolspec_head.param_schema_adapter.lora_B.default", | |
| "toolspec_head.param_schema_adapter.lora_dropout", | |
| "toolspec_head.param_schema_adapter.lora_dropout.default", | |
| "toolspec_head.param_schema_adapter.lora_embedding_A", | |
| "toolspec_head.param_schema_adapter.lora_embedding_B", | |
| "toolspec_head.param_schema_adapter.lora_magnitude_vector", | |
| "toolspec_head.postcondition_adapter", | |
| "toolspec_head.postcondition_adapter.base_layer", | |
| "toolspec_head.postcondition_adapter.lora_A", | |
| "toolspec_head.postcondition_adapter.lora_A.default", | |
| "toolspec_head.postcondition_adapter.lora_B", | |
| "toolspec_head.postcondition_adapter.lora_B.default", | |
| "toolspec_head.postcondition_adapter.lora_dropout", | |
| "toolspec_head.postcondition_adapter.lora_dropout.default", | |
| "toolspec_head.postcondition_adapter.lora_embedding_A", | |
| "toolspec_head.postcondition_adapter.lora_embedding_B", | |
| "toolspec_head.postcondition_adapter.lora_magnitude_vector", | |
| "toolspec_head.precondition_adapter", | |
| "toolspec_head.precondition_adapter.base_layer", | |
| "toolspec_head.precondition_adapter.lora_A", | |
| "toolspec_head.precondition_adapter.lora_A.default", | |
| "toolspec_head.precondition_adapter.lora_B", | |
| "toolspec_head.precondition_adapter.lora_B.default", | |
| "toolspec_head.precondition_adapter.lora_dropout", | |
| "toolspec_head.precondition_adapter.lora_dropout.default", | |
| "toolspec_head.precondition_adapter.lora_embedding_A", | |
| "toolspec_head.precondition_adapter.lora_embedding_B", | |
| "toolspec_head.precondition_adapter.lora_magnitude_vector", | |
| "toolspec_head.receipt_formatter", | |
| "toolspec_head.receipt_formatter.base_layer", | |
| "toolspec_head.receipt_formatter.lora_A", | |
| "toolspec_head.receipt_formatter.lora_A.default", | |
| "toolspec_head.receipt_formatter.lora_B", | |
| "toolspec_head.receipt_formatter.lora_B.default", | |
| "toolspec_head.receipt_formatter.lora_dropout", | |
| "toolspec_head.receipt_formatter.lora_dropout.default", | |
| "toolspec_head.receipt_formatter.lora_embedding_A", | |
| "toolspec_head.receipt_formatter.lora_embedding_B", | |
| "toolspec_head.receipt_formatter.lora_magnitude_vector", | |
| "toolspec_head.trigger_gate", | |
| "toolspec_head.trigger_gate.base_layer", | |
| "toolspec_head.trigger_gate.lora_A", | |
| "toolspec_head.trigger_gate.lora_A.default", | |
| "toolspec_head.trigger_gate.lora_B", | |
| "toolspec_head.trigger_gate.lora_B.default", | |
| "toolspec_head.trigger_gate.lora_dropout", | |
| "toolspec_head.trigger_gate.lora_dropout.default", | |
| "toolspec_head.trigger_gate.lora_embedding_A", | |
| "toolspec_head.trigger_gate.lora_embedding_B", | |
| "toolspec_head.trigger_gate.lora_magnitude_vector", | |
| "toolspec_head.validator_gate", | |
| "toolspec_head.validator_gate.base_layer", | |
| "toolspec_head.validator_gate.lora_A", | |
| "toolspec_head.validator_gate.lora_A.default", | |
| "toolspec_head.validator_gate.lora_B", | |
| "toolspec_head.validator_gate.lora_B.default", | |
| "toolspec_head.validator_gate.lora_dropout", | |
| "toolspec_head.validator_gate.lora_dropout.default", | |
| "toolspec_head.validator_gate.lora_embedding_A", | |
| "toolspec_head.validator_gate.lora_embedding_B", | |
| "toolspec_head.validator_gate.lora_magnitude_vector", | |
| "uncertainty_head", | |
| "uncertainty_head.calibration_mlp", | |
| "uncertainty_head.proj", | |
| "validator_feedback_bridge", | |
| "validator_feedback_bridge.adapter", | |
| "validator_feedback_bridge.adapter.base_layer", | |
| "validator_feedback_bridge.adapter.lora_A", | |
| "validator_feedback_bridge.adapter.lora_A.default", | |
| "validator_feedback_bridge.adapter.lora_B", | |
| "validator_feedback_bridge.adapter.lora_B.default", | |
| "validator_feedback_bridge.adapter.lora_dropout", | |
| "validator_feedback_bridge.adapter.lora_dropout.default", | |
| "validator_feedback_bridge.adapter.lora_embedding_A", | |
| "validator_feedback_bridge.adapter.lora_embedding_B", | |
| "validator_feedback_bridge.adapter.lora_magnitude_vector", | |
| "validator_feedback_loop", | |
| "validator_feedback_loop.rollback_adapter", | |
| "validator_receipt_bridge", | |
| "validator_receipt_bridge.adapter", | |
| "validator_uncertainty_bridge", | |
| "validator_uncertainty_bridge.adapter", | |
| "workspace_snapshot_manager", | |
| "workspace_snapshot_manager.rollback_context", | |
| "worktree_binding", | |
| "worktree_binding.adapter", | |
| "worktree_manager", | |
| "worktree_manager.adapter" | |
| ], | |
| "trainable_parameter_report": { | |
| "backbone_frozen": true, | |
| "backbone_present": true, | |
| "hidden_size": 2048, | |
| "host_parameters": 176621573, | |
| "host_trainable_parameters": 360448, | |
| "total_parameters": 34132679301, | |
| "trainable_parameters": 360448, | |
| "version": "veriloop.coder_peft_host.v1" | |
| }, | |
| "version": "veriloop.coder_peft_host.v1" | |
| } |