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
| { | |
| "backbone": "/public/wang_libo/veriloop_coder_e1/model", | |
| "dataset_summary": { | |
| "eval_size": 25, | |
| "modes": [ | |
| "argument_shaping", | |
| "harness_envelope", | |
| "precondition_gating", | |
| "receipt_and_validation", | |
| "reverse_engineering", | |
| "runtime_session", | |
| "self_check_loop", | |
| "tool_trigger", | |
| "worktree_permission" | |
| ], | |
| "tools": [ | |
| "apply_patch_bundle", | |
| "browser_search", | |
| "repo_reverse_engineer", | |
| "run_ci_validation", | |
| "self_check_repair_loop" | |
| ], | |
| "train_size": 70 | |
| }, | |
| "excluded_surfaces": [ | |
| "(^|\\.)lm_head($|\\.)::Do not retune final token head; too broad and evaluation-heavy.", | |
| "(^|\\.)embed_tokens($|\\.)::Embedding surgery risks broad semantic drift.", | |
| "(^|\\.)norm($|\\.)::Global norm tuning can destabilize calibration across scenes.", | |
| "attnres|attention_residual::Block AttnRes may be mounted structurally but is never a PEFT target.", | |
| "dualpath::DualPath is serving/runtime infrastructure only.", | |
| "mhc|hyper[-_]?connection::mHC-inspired stability hooks remain structural, not PEFT surfaces.", | |
| "rope|rotary::RoPE/context surgery is handled architecturally, not by narrow PEFT here.", | |
| "kvcache|kv_cache::KV-cache runtime surfaces are not PEFT targets.", | |
| "(^|\\.)memory(_store|_bank)?($|\\.)::Persistent memory stores are harness/runtime policy surfaces, not PEFT targets." | |
| ], | |
| "notes": [ | |
| "Primary route is host-surface-first toolspec probe training, not broad coding SFT.", | |
| "This adapter is allowed to shape runtime protocol obedience, validator alignment, self-check willingness, and reverse-engineering readiness.", | |
| "Backbone attention, MoE router/expert weights, DualPath, Block AttnRes, and mHC remain excluded.", | |
| "Explicit host/runtime bridge leaves default to LoRA-narrow attachment because IA3 feedforward constraints do not fit this custom side-car surface design." | |
| ], | |
| "peft_method": "lora_narrow", | |
| "product_line": "veriloop_coder", | |
| "recipe": { | |
| "adapter_family": "runtime_harness", | |
| "backbone": "/public/wang_libo/veriloop_coder_e1/model", | |
| "backbone_family": "qwen_dense", | |
| "excluded_patterns": [ | |
| "(?i)\\bdualpath\\b", | |
| "(?i)\\bmhc\\b", | |
| "(?i)\\bfull[_\\- ]?attnres\\b", | |
| "(?i)\\battnres(_full)?\\b", | |
| "(?i)\\brouter\\b", | |
| "(?i)\\bexperts?\\b", | |
| "(?i)\\bmoe\\b.*\\b(gate|router|expert)\\b", | |
| "(?i)\\brope\\b.*\\b(freq|inv_freq|theta|rotary)\\b", | |
| "(?i)\\bkvcache\\b", | |
| "(?i)\\bposition_embedding\\b", | |
| "(?i)\\bembed(tokens|ding)?\\b", | |
| "(?i)\\blm_head\\b" | |
| ], | |
| "harness_constraints": [ | |
| "Harness Engineering remains the primary convergence layer.", | |
| "Adapter must not bypass runtime orchestrator / validator / rollback loops.", | |
| "Adapter outputs remain subordinate to VeriLoop control-plane decisions.", | |
| "Adapter must not create hidden prompt-style memory authority.", | |
| "Adapter must improve runtime protocol obedience, not free-form style drift.", | |
| "Tool legality, permission discipline, session continuity, and worktree hygiene must remain first-class." | |
| ], | |
| "hyperparams": { | |
| "alpha": 0, | |
| "bias": "none", | |
| "dropout": 0.0, | |
| "fan_in_fan_out": false, | |
| "modules_to_save": [], | |
| "r": 0, | |
| "task_type": "CAUSAL_LM" | |
| }, | |
| "merge_policy": "side_load", | |
| "metadata": { | |
| "allow_backbone_bridge": false, | |
| "allow_vla_action_expert": false, | |
| "harness_first": true, | |
| "prefer_explicit_heads": true, | |
| "prefer_qlora_for_backbone_bridge": true, | |
| "require_harness_first": true, | |
| "selector_group_count": 2, | |
| "strict_narrow_scope": true, | |
| "toolspec_probe_training": true, | |
| "trainer": "veriloop.toolspec_adapter_trainer.v5.qwen36" | |
| }, | |
| "notes": [ | |
| "Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.", | |
| "Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op.", | |
| "Backbone family inferred as qwen_dense.", | |
| "PEFT method resolved as ia3_head_only.", | |
| "Recipe is harness-first: runtime convergence remains in VeriLoop control-plane + harness, not in broad weight surgery.", | |
| "Block AttnRes, DualPath, mHC hooks, RoPE, KV-cache, and broad MoE routing remain structurally excluded." | |
| ], | |
| "peft_method": "lora_narrow", | |
| "precision_policy": "auto", | |
| "product_line": "veriloop_coder", | |
| "regression_requirements": [ | |
| "Must pass PEFT regression guard structural policy checks.", | |
| "Must not introduce forbidden backbone/serving structural targets.", | |
| "Must preserve harness regression envelope for the selected product line.", | |
| "Runtime protocol obedience must improve or hold.", | |
| "Tool trigger accuracy, permission discipline, and worktree continuity must not regress." | |
| ], | |
| "target_groups": [ | |
| { | |
| "alpha": 16, | |
| "dropout": 0.05, | |
| "name": "group_1_custom_runtime_harness_bridge", | |
| "rank": 8, | |
| "rationale": "Runtime / harness obedience should attach to explicit interface bridges before any backbone fallback.", | |
| "surface": "custom_runtime_harness_bridge", | |
| "target_modules": [ | |
| "failure_signal_bridge.rollback_bridge", | |
| "request_normalizer", | |
| "request_normalizer.adapter", | |
| "rollback_adapter", | |
| "rollback_adapter.head", | |
| "runtime_harness_adapter", | |
| "runtime_harness_adapter.bridge", | |
| "tool_protocol_adapter", | |
| "tool_protocol_adapter.bridge", | |
| "toolspec_bridge", | |
| "toolspec_bridge.adapter", | |
| "toolspec_head", | |
| "toolspec_head.param_schema_adapter", | |
| "toolspec_head.postcondition_adapter", | |
| "toolspec_head.precondition_adapter", | |
| "toolspec_head.receipt_formatter", | |
| "toolspec_head.trigger_gate", | |
| "toolspec_head.validator_gate", | |
| "validator_feedback_loop.rollback_adapter" | |
| ] | |
| }, | |
| { | |
| "alpha": 8, | |
| "dropout": 0.0, | |
| "name": "group_2_custom_memory_boundary_bridge", | |
| "rank": 4, | |
| "rationale": "Session continuity should bind to boundary-aware memory packet surfaces rather than broad backbone tuning.", | |
| "surface": "custom_memory_boundary_bridge", | |
| "target_modules": [ | |
| "episodic_memory", | |
| "episodic_memory.adapter", | |
| "memory_boundary_guard", | |
| "memory_boundary_guard.adapter", | |
| "memory_boundary_guard.rollback_filter", | |
| "session_compactor", | |
| "session_compactor.adapter" | |
| ] | |
| } | |
| ], | |
| "target_modules": [ | |
| "failure_signal_bridge.rollback_bridge", | |
| "request_normalizer", | |
| "request_normalizer.adapter", | |
| "rollback_adapter", | |
| "rollback_adapter.head", | |
| "runtime_harness_adapter", | |
| "runtime_harness_adapter.bridge", | |
| "tool_protocol_adapter", | |
| "tool_protocol_adapter.bridge", | |
| "toolspec_bridge", | |
| "toolspec_bridge.adapter", | |
| "toolspec_head", | |
| "toolspec_head.param_schema_adapter", | |
| "toolspec_head.postcondition_adapter", | |
| "toolspec_head.precondition_adapter", | |
| "toolspec_head.receipt_formatter", | |
| "toolspec_head.trigger_gate", | |
| "toolspec_head.validator_gate", | |
| "validator_feedback_loop.rollback_adapter", | |
| "episodic_memory", | |
| "episodic_memory.adapter", | |
| "memory_boundary_guard", | |
| "memory_boundary_guard.adapter", | |
| "memory_boundary_guard.rollback_filter", | |
| "session_compactor", | |
| "session_compactor.adapter" | |
| ], | |
| "version": "veriloop.lora_recipe_veriloop.v2" | |
| }, | |
| "selected_surfaces": [ | |
| "custom_runtime_harness_bridge", | |
| "custom_validator_bridge", | |
| "custom_memory_boundary_bridge" | |
| ], | |
| "selected_target_modules": [ | |
| "request_normalizer", | |
| "request_normalizer.adapter", | |
| "runtime_harness_adapter", | |
| "runtime_harness_adapter.bridge", | |
| "tool_protocol_adapter", | |
| "tool_protocol_adapter.bridge", | |
| "toolspec_bridge", | |
| "toolspec_bridge.adapter", | |
| "toolspec_head", | |
| "toolspec_head.param_schema_adapter", | |
| "toolspec_head.postcondition_adapter", | |
| "toolspec_head.precondition_adapter", | |
| "toolspec_head.receipt_formatter", | |
| "toolspec_head.trigger_gate", | |
| "toolspec_head.validator_gate", | |
| "validator_feedback_bridge", | |
| "validator_feedback_bridge.adapter" | |
| ], | |
| "selection_mode": "minimal", | |
| "version": "veriloop.toolspec_adapter_trainer.v5.qwen36", | |
| "warnings": [ | |
| "Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.", | |
| "Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op." | |
| ] | |
| } |