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
Delete toolspec_adapter_train_result.json
Browse files
toolspec_adapter_train_result.json
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{
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"artifacts": {
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"adapter_dir": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/adapter",
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"best_checkpoint_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/best_checkpoint_manifest.json",
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"epoch_history": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/epoch_history.json",
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"eval_jsonl": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/toolspec_eval.jsonl",
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"host_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/host_manifest.json",
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"plan_json": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/toolspec_adapter_plan.json",
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"probe_head": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/toolspec_probe_head.pt",
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"tokenizer_dir": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/tokenizer",
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"train_jsonl": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/toolspec_train.jsonl",
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"training_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/toolspec_qwen36_rootfix_run1/toolspec_training_manifest.json"
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},
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"dataset": {
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"eval_size": 25,
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"modes": [
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"argument_shaping",
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"harness_envelope",
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"precondition_gating",
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"receipt_and_validation",
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"reverse_engineering",
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"runtime_session",
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"self_check_loop",
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"tool_trigger",
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"worktree_permission"
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],
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"tools": [
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"apply_patch_bundle",
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"browser_search",
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"repo_reverse_engineer",
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"run_ci_validation",
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"self_check_repair_loop"
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],
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"train_size": 70
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},
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"eval_metrics": {
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"adapter_exported": true,
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"auto_lora_from_ia3": false,
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"avg_binary_accuracy": 0.81,
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"best_epoch": 4,
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"best_quality_score": 0.46349615617231893,
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"confirmation_required_accuracy": 0.8,
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"count": 25,
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"eval_batches": 25,
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"eval_loss": 6.897225952148437,
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"mode_accuracy": 0.56,
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"peft_method": "lora_narrow",
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"permission_bit_accuracy": 0.8545454545454545,
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"precondition_ok_accuracy": 0.76,
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"quality_score": 0.46349615617231893,
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"rollback_supported_accuracy": 0.8,
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"schema_ok_accuracy": 0.76,
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"session_required_accuracy": 1.0,
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"tool_accuracy": 0.44,
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"trigger_accuracy": 0.76,
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"used_peft": true,
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"validator_required_accuracy": 0.8,
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"worktree_required_accuracy": 0.8
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},
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"plan": {
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"backbone": "/public/wang_libo/veriloop_coder_e1/model",
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"dataset_summary": {
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"eval_size": 25,
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"modes": [
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"argument_shaping",
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"harness_envelope",
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"precondition_gating",
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"receipt_and_validation",
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"reverse_engineering",
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"runtime_session",
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"self_check_loop",
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"tool_trigger",
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"worktree_permission"
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],
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"tools": [
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"apply_patch_bundle",
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"browser_search",
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"repo_reverse_engineer",
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"run_ci_validation",
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"self_check_repair_loop"
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],
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"train_size": 70
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},
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"excluded_surfaces": [
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"(^|\\.)lm_head($|\\.)::Do not retune final token head; too broad and evaluation-heavy.",
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"(^|\\.)embed_tokens($|\\.)::Embedding surgery risks broad semantic drift.",
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"(^|\\.)norm($|\\.)::Global norm tuning can destabilize calibration across scenes.",
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"attnres|attention_residual::Block AttnRes may be mounted structurally but is never a PEFT target.",
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"dualpath::DualPath is serving/runtime infrastructure only.",
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"mhc|hyper[-_]?connection::mHC-inspired stability hooks remain structural, not PEFT surfaces.",
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"rope|rotary::RoPE/context surgery is handled architecturally, not by narrow PEFT here.",
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"kvcache|kv_cache::KV-cache runtime surfaces are not PEFT targets.",
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"(^|\\.)memory(_store|_bank)?($|\\.)::Persistent memory stores are harness/runtime policy surfaces, not PEFT targets."
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],
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"notes": [
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"Primary route is host-surface-first toolspec probe training, not broad coding SFT.",
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"This adapter is allowed to shape runtime protocol obedience, validator alignment, self-check willingness, and reverse-engineering readiness.",
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"Backbone attention, MoE router/expert weights, DualPath, Block AttnRes, and mHC remain excluded.",
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"Explicit host/runtime bridge leaves default to LoRA-narrow attachment because IA3 feedforward constraints do not fit this custom side-car surface design."
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],
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"peft_method": "lora_narrow",
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"product_line": "veriloop_coder",
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"recipe": {
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"adapter_family": "runtime_harness",
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"backbone": "/public/wang_libo/veriloop_coder_e1/model",
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"backbone_family": "qwen_dense",
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"excluded_patterns": [
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"(?i)\\bdualpath\\b",
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"(?i)\\bmhc\\b",
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"(?i)\\bfull[_\\- ]?attnres\\b",
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"(?i)\\battnres(_full)?\\b",
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"(?i)\\brouter\\b",
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"(?i)\\bexperts?\\b",
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"(?i)\\bmoe\\b.*\\b(gate|router|expert)\\b",
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"(?i)\\brope\\b.*\\b(freq|inv_freq|theta|rotary)\\b",
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"(?i)\\bkvcache\\b",
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"(?i)\\bposition_embedding\\b",
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"(?i)\\bembed(tokens|ding)?\\b",
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"(?i)\\blm_head\\b"
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],
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"harness_constraints": [
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"Harness Engineering remains the primary convergence layer.",
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"Adapter must not bypass runtime orchestrator / validator / rollback loops.",
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"Adapter outputs remain subordinate to VeriLoop control-plane decisions.",
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"Adapter must not create hidden prompt-style memory authority.",
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"Adapter must improve runtime protocol obedience, not free-form style drift.",
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"Tool legality, permission discipline, session continuity, and worktree hygiene must remain first-class."
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],
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"hyperparams": {
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"alpha": 0,
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"bias": "none",
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"dropout": 0.0,
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"fan_in_fan_out": false,
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"modules_to_save": [],
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"r": 0,
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"task_type": "CAUSAL_LM"
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},
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"merge_policy": "side_load",
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"metadata": {
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"allow_backbone_bridge": false,
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"allow_vla_action_expert": false,
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"harness_first": true,
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"prefer_explicit_heads": true,
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"prefer_qlora_for_backbone_bridge": true,
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"require_harness_first": true,
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"selector_group_count": 2,
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"strict_narrow_scope": true,
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"toolspec_probe_training": true,
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"trainer": "veriloop.toolspec_adapter_trainer.v5.qwen36"
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},
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"notes": [
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"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
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"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op.",
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"Backbone family inferred as qwen_dense.",
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"PEFT method resolved as ia3_head_only.",
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"Recipe is harness-first: runtime convergence remains in VeriLoop control-plane + harness, not in broad weight surgery.",
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"Block AttnRes, DualPath, mHC hooks, RoPE, KV-cache, and broad MoE routing remain structurally excluded."
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],
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"peft_method": "lora_narrow",
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"precision_policy": "auto",
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"product_line": "veriloop_coder",
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"regression_requirements": [
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"Must pass PEFT regression guard structural policy checks.",
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"Must not introduce forbidden backbone/serving structural targets.",
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"Must preserve harness regression envelope for the selected product line.",
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"Runtime protocol obedience must improve or hold.",
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"Tool trigger accuracy, permission discipline, and worktree continuity must not regress."
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],
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"target_groups": [
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{
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"alpha": 16,
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"dropout": 0.05,
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"name": "group_1_custom_runtime_harness_bridge",
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"rank": 8,
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"rationale": "Runtime / harness obedience should attach to explicit interface bridges before any backbone fallback.",
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"surface": "custom_runtime_harness_bridge",
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"target_modules": [
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"failure_signal_bridge.rollback_bridge",
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"request_normalizer",
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"request_normalizer.adapter",
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"rollback_adapter",
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"rollback_adapter.head",
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"runtime_harness_adapter",
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"runtime_harness_adapter.bridge",
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"tool_protocol_adapter",
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"tool_protocol_adapter.bridge",
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"toolspec_bridge",
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"toolspec_bridge.adapter",
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"toolspec_head",
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"toolspec_head.param_schema_adapter",
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"toolspec_head.postcondition_adapter",
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"toolspec_head.precondition_adapter",
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"toolspec_head.receipt_formatter",
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"toolspec_head.trigger_gate",
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"toolspec_head.validator_gate",
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"validator_feedback_loop.rollback_adapter"
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]
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},
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{
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"alpha": 8,
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"dropout": 0.0,
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"name": "group_2_custom_memory_boundary_bridge",
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"rank": 4,
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"rationale": "Session continuity should bind to boundary-aware memory packet surfaces rather than broad backbone tuning.",
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"surface": "custom_memory_boundary_bridge",
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"target_modules": [
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"episodic_memory",
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"episodic_memory.adapter",
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"memory_boundary_guard",
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"memory_boundary_guard.adapter",
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"memory_boundary_guard.rollback_filter",
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"session_compactor",
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"session_compactor.adapter"
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]
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}
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],
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"target_modules": [
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"failure_signal_bridge.rollback_bridge",
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"request_normalizer",
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"request_normalizer.adapter",
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"rollback_adapter",
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"rollback_adapter.head",
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"runtime_harness_adapter",
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"runtime_harness_adapter.bridge",
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"tool_protocol_adapter",
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"tool_protocol_adapter.bridge",
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"toolspec_bridge",
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"toolspec_bridge.adapter",
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"toolspec_head",
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"toolspec_head.param_schema_adapter",
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"toolspec_head.postcondition_adapter",
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"toolspec_head.precondition_adapter",
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"toolspec_head.receipt_formatter",
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| 234 |
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"toolspec_head.trigger_gate",
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"toolspec_head.validator_gate",
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"validator_feedback_loop.rollback_adapter",
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"episodic_memory",
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"episodic_memory.adapter",
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"memory_boundary_guard",
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| 240 |
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"memory_boundary_guard.adapter",
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| 241 |
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"memory_boundary_guard.rollback_filter",
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"session_compactor",
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"session_compactor.adapter"
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],
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"version": "veriloop.lora_recipe_veriloop.v2"
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},
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"selected_surfaces": [
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"custom_runtime_harness_bridge",
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"custom_validator_bridge",
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"custom_memory_boundary_bridge"
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],
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| 252 |
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"selected_target_modules": [
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"request_normalizer",
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"request_normalizer.adapter",
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| 255 |
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"runtime_harness_adapter",
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"runtime_harness_adapter.bridge",
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"tool_protocol_adapter",
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"tool_protocol_adapter.bridge",
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"toolspec_bridge",
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"toolspec_bridge.adapter",
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"toolspec_head",
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"toolspec_head.param_schema_adapter",
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| 263 |
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"toolspec_head.postcondition_adapter",
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| 264 |
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"toolspec_head.precondition_adapter",
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| 265 |
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"toolspec_head.receipt_formatter",
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| 266 |
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"toolspec_head.trigger_gate",
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| 267 |
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"toolspec_head.validator_gate",
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| 268 |
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"validator_feedback_bridge",
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| 269 |
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"validator_feedback_bridge.adapter"
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| 270 |
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],
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| 271 |
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"selection_mode": "minimal",
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| 272 |
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"version": "veriloop.toolspec_adapter_trainer.v5.qwen36",
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| 273 |
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"warnings": [
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| 274 |
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"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
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| 275 |
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"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op."
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| 276 |
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]
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},
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"status": "trained",
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"train_metrics": {
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"adapter_exported": true,
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| 281 |
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"auto_lora_from_ia3": false,
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| 282 |
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"best_epoch": 4,
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| 283 |
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"best_quality_score": 0.46349615617231893,
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| 284 |
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"epochs_completed": 5,
|
| 285 |
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"loss": 0.42175399448190415,
|
| 286 |
-
"micro_batches": 70,
|
| 287 |
-
"micro_batches_total": 350,
|
| 288 |
-
"optimizer_steps": 5,
|
| 289 |
-
"optimizer_steps_total": 25,
|
| 290 |
-
"peft_method": "lora_narrow",
|
| 291 |
-
"used_peft": true
|
| 292 |
-
},
|
| 293 |
-
"version": "veriloop.toolspec_adapter_trainer.v5.qwen36",
|
| 294 |
-
"warnings": [
|
| 295 |
-
"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
|
| 296 |
-
"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op.",
|
| 297 |
-
"Synthetic holdout split used for quality-oriented train/eval separation."
|
| 298 |
-
]
|
| 299 |
-
}
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