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
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"backbone": "/public/wang_libo/veriloop_coder_e1/model",
"dataset_summary": {
"cause_vocab": [
"none",
"evidence_contradiction",
"execution_negation",
"validator_rejection",
"patch_regression",
"worktree_conflict",
"high_risk_unsupported",
"tool_selfcheck_failure",
"reverse_engineering_speculation"
],
"eval_size": 45,
"mode_vocab": [
"contradiction",
"execution_failure",
"validator_rejection",
"patch_regression",
"worktree_conflict",
"high_risk_unsupported",
"tool_selfcheck_repair",
"reverse_engineering_drift",
"validated_stable"
],
"modes": [
"contradiction",
"execution_failure",
"high_risk_unsupported",
"patch_regression",
"reverse_engineering_drift",
"tool_selfcheck_repair",
"validated_stable",
"validator_rejection",
"worktree_conflict"
],
"need_vocab": [
"none",
"advisory",
"required"
],
"rewrite_vocab": [
"do_not_rollback",
"local_patch_only",
"validator_scoped_revision",
"minimum_diff_patch",
"do_not_overwrite_worktree_state",
"fail_closed",
"repair_via_bounded_selfcheck_loop",
"bounded_observation_first",
"local_conflict_preserving_revision"
],
"train_size": 126
},
"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 rollback training.",
"Validator receipts, failure routing, patch state, worktree continuity, self-check repair, and reverse-engineering boundedness are first-class signals.",
"DualPath, Block AttnRes, mHC hooks, and MoE routers/experts remain structurally excluded.",
"This adapter should improve rollback obedience, not general coding free-formity."
],
"peft_method": "lora_narrow",
"product_line": "veriloop_coder",
"recipe": {
"adapter_family": "validator_rollback",
"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 preserve validator-first correction and minimal-diff rollback discipline.",
"Patch production must remain subordinate to validator receipts and failure routing."
],
"hyperparams": {
"alpha": 16,
"bias": "none",
"dropout": 0.08,
"fan_in_fan_out": false,
"modules_to_save": [
"input_layernorm"
],
"r": 8,
"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": false,
"require_harness_first": true,
"reverse_engineering_readiness": true,
"rollback_training": true,
"selector_group_count": 2,
"selfcheck_loop_readiness": true,
"strict_narrow_scope": true,
"trainer": "veriloop.rollback_adapter_trainer.v9.qwen36"
},
"notes": [
"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op.",
"Backbone family inferred as qwen_dense.",
"PEFT method resolved as lora_narrow.",
"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.",
"Validator feedback uptake must not regress.",
"Rollback minimality and revision fidelity 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": 16,
"dropout": 0.0,
"name": "group_2_custom_validator_bridge",
"rank": 8,
"rationale": "Validation and rollback fidelity should prefer explicit validator / rollback bridges.",
"surface": "custom_validator_bridge",
"target_modules": [
"failure_signal_bridge",
"failure_signal_bridge.rollback_bridge",
"rollback_adapter",
"rollback_adapter.head",
"sandbox_rollback_bridge",
"sandbox_rollback_bridge.adapter",
"validator_feedback_bridge",
"validator_feedback_bridge.adapter",
"validator_feedback_loop.rollback_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",
"failure_signal_bridge",
"sandbox_rollback_bridge",
"sandbox_rollback_bridge.adapter",
"validator_feedback_bridge",
"validator_feedback_bridge.adapter"
],
"version": "veriloop.lora_recipe_veriloop.v2"
},
"selected_surfaces": [
"custom_runtime_harness_bridge",
"custom_validator_bridge",
"custom_memory_boundary_bridge"
],
"selected_target_modules": [
"failure_signal_bridge.rollback_bridge",
"rollback_adapter.head",
"runtime_harness_adapter.bridge",
"sandbox_rollback_bridge.adapter",
"toolspec_head.validator_gate",
"validator_feedback_bridge.adapter",
"validator_feedback_loop.rollback_adapter",
"memory_boundary_guard.adapter",
"memory_boundary_guard.rollback_filter",
"request_normalizer.adapter",
"tool_protocol_adapter.bridge",
"toolspec_bridge.adapter",
"toolspec_head.postcondition_adapter",
"toolspec_head.precondition_adapter",
"toolspec_head.receipt_formatter",
"toolspec_head.trigger_gate"
],
"selection_mode": "minimal",
"version": "veriloop.rollback_adapter_trainer.v9.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."
]
} |