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 uncertainty_adapter_train_result.json
Browse files
uncertainty_adapter_train_result.json
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{
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"artifacts": {
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"best_checkpoint_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/best_checkpoint_manifest.json",
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"epoch_history": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/epoch_history.json",
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"eval_jsonl": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/uncertainty_eval.jsonl",
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"host_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/host_manifest.json",
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"plan_json": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/uncertainty_adapter_plan.json",
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"tokenizer_dir": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/tokenizer",
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"train_jsonl": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/uncertainty_train.jsonl",
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"training_manifest": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/uncertainty_training_manifest.json",
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"uncertainty_head": "/private/wang_libo/veriloop_coder_e1/outputs/uncertainty_qwen36_rootfix_run1/uncertainty_head.pt"
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},
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"dataset": {
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},
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"eval_size": 55,
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},
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"train_size": 154
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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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"best_epoch": 2,
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"best_quality_score": 0.8625935807221907,
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"count": 55,
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},
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"moderate_accuracy": 0.6727272727272727,
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"peft_method": "lora_narrow",
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"quality_score": 0.8625935807221907,
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"rmse": {
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},
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"tight_accuracy": 0.4,
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"used_peft": true,
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"weighted_mae": 0.18083095811830807,
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"weighted_rmse": 0.24125460771003793
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},
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"plan": {
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"adapter_alpha": 16,
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"adapter_dropout": 0.05,
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"adapter_rank": 8,
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"backbone_manifest": {
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"host_inventory_size": 79
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},
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"config": {
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"allow_backbone_bridge": false,
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"backbone": "/public/wang_libo/veriloop_coder_e1/model",
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"bf16": true,
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"cache_dir": null,
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"u_spec": 1.25
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},
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"early_stopping_min_delta": 0.001,
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"fp16": false,
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"host_dropout": 0.0,
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"learning_rate": 2e-05,
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"local_files_only": true,
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"output_dir": "./outputs/uncertainty_qwen36_rootfix_run1",
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"per_device_eval_batch_size": 1,
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"per_device_train_batch_size": 1,
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"prefer_best_checkpoint_export": true,
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"probe_dropout": 0.03,
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"product_line": "veriloop_coder",
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"quantization_mode": "4bit",
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"quantization_required": false,
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"require_trainable_targets": true,
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},
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"dataset_summary": {
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},
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},
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"train_size": 154
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},
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"head_strategy": "host_head",
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"notes": [
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"Primary route is host-surface-first uncertainty training.",
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"Validator receipts, self-check failure, reverse-engineering ambiguity, worktree conflicts, and patch continuity are first-class signals.",
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"DualPath, Full AttnRes, mHC, routers, experts, and broad attention-layer PEFT stay excluded.",
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"This adapter should improve runtime uncertainty routing, not general coding free-formity."
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],
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"peft_method": "lora_narrow",
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"recipe": {
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"adapter_family": "uncertainty",
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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 support bounded uncertainty calibration rather than generic hesitation.",
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"Validator and receipt evidence must remain able to update uncertainty."
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],
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"hyperparams": {
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"alpha": 16,
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"bias": "none",
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"dropout": 0.05,
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"fan_in_fan_out": false,
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"modules_to_save": [
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"input_layernorm"
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],
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"r": 8,
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"task_type": "CAUSAL_LM"
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},
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"merge_policy": "merge_after_guard",
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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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| 216 |
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"prefer_qlora_for_backbone_bridge": true,
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"require_harness_first": true,
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| 218 |
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"selector_group_count": 2,
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| 219 |
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"strict_narrow_scope": true,
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"trainer": "veriloop.uncertainty_adapter_trainer.v5.qwen36",
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"uncertainty_training": true
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},
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"notes": [
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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 lora_narrow.",
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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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"Budgeted uncertainty convergence must not regress.",
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"Uncertainty calibration must not collapse into generic caution."
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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": 0.0,
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| 244 |
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"name": "group_1_custom_control_head",
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"rank": 8,
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"rationale": "Prefer explicit uncertainty / calibration heads over backbone surgery.",
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"surface": "custom_control_head",
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"target_modules": [
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"uncertainty_head",
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"uncertainty_head.calibration_mlp",
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"uncertainty_head.proj"
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]
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},
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{
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"alpha": 16,
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"dropout": 0.0,
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"name": "group_2_custom_validator_bridge",
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"rank": 8,
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"rationale": "Validation and rollback fidelity should prefer explicit validator / rollback bridges.",
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"surface": "custom_validator_bridge",
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"target_modules": [
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"failure_signal_bridge",
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"failure_signal_bridge.rollback_bridge",
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"rollback_adapter",
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"rollback_adapter.head",
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"sandbox_rollback_bridge",
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"sandbox_rollback_bridge.adapter",
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"validator_feedback_bridge",
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"validator_feedback_bridge.adapter",
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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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"target_modules": [
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"uncertainty_head",
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"uncertainty_head.calibration_mlp",
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"uncertainty_head.proj",
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"failure_signal_bridge",
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"failure_signal_bridge.rollback_bridge",
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"rollback_adapter",
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"rollback_adapter.head",
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| 282 |
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"sandbox_rollback_bridge",
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"sandbox_rollback_bridge.adapter",
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| 284 |
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"validator_feedback_bridge",
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| 285 |
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"validator_feedback_bridge.adapter",
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| 286 |
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"validator_feedback_loop.rollback_adapter"
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],
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"version": "veriloop.lora_recipe_veriloop.v2"
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| 289 |
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},
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| 290 |
-
"selected_target_modules": [
|
| 291 |
-
"uncertainty_head",
|
| 292 |
-
"uncertainty_head.calibration_mlp",
|
| 293 |
-
"uncertainty_head.proj"
|
| 294 |
-
],
|
| 295 |
-
"target_selection": {
|
| 296 |
-
"backbone_archetype": "qwen_dense",
|
| 297 |
-
"exclusions": [
|
| 298 |
-
{
|
| 299 |
-
"pattern": "(^|\\.)lm_head($|\\.)",
|
| 300 |
-
"reason": "Do not retune final token head; too broad and evaluation-heavy."
|
| 301 |
-
},
|
| 302 |
-
{
|
| 303 |
-
"pattern": "(^|\\.)embed_tokens($|\\.)",
|
| 304 |
-
"reason": "Embedding surgery risks broad semantic drift."
|
| 305 |
-
},
|
| 306 |
-
{
|
| 307 |
-
"pattern": "(^|\\.)norm($|\\.)",
|
| 308 |
-
"reason": "Global norm tuning can destabilize calibration across scenes."
|
| 309 |
-
},
|
| 310 |
-
{
|
| 311 |
-
"pattern": "attnres|attention_residual",
|
| 312 |
-
"reason": "Block AttnRes may be mounted structurally but is never a PEFT target."
|
| 313 |
-
},
|
| 314 |
-
{
|
| 315 |
-
"pattern": "dualpath",
|
| 316 |
-
"reason": "DualPath is serving/runtime infrastructure only."
|
| 317 |
-
},
|
| 318 |
-
{
|
| 319 |
-
"pattern": "mhc|hyper[-_]?connection",
|
| 320 |
-
"reason": "mHC-inspired stability hooks remain structural, not PEFT surfaces."
|
| 321 |
-
},
|
| 322 |
-
{
|
| 323 |
-
"pattern": "rope|rotary",
|
| 324 |
-
"reason": "RoPE/context surgery is handled architecturally, not by narrow PEFT here."
|
| 325 |
-
},
|
| 326 |
-
{
|
| 327 |
-
"pattern": "kvcache|kv_cache",
|
| 328 |
-
"reason": "KV-cache runtime surfaces are not PEFT targets."
|
| 329 |
-
},
|
| 330 |
-
{
|
| 331 |
-
"pattern": "(^|\\.)memory(_store|_bank)?($|\\.)",
|
| 332 |
-
"reason": "Persistent memory stores are harness/runtime policy surfaces, not PEFT targets."
|
| 333 |
-
}
|
| 334 |
-
],
|
| 335 |
-
"inventory_size": 79,
|
| 336 |
-
"inventory_source": "provided_names",
|
| 337 |
-
"notes": [
|
| 338 |
-
"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
|
| 339 |
-
"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op."
|
| 340 |
-
],
|
| 341 |
-
"recommended_training": "ia3_head_only",
|
| 342 |
-
"request": {
|
| 343 |
-
"allow_backbone_bridge": false,
|
| 344 |
-
"allow_vla_action_expert": false,
|
| 345 |
-
"backbone": "/public/wang_libo/veriloop_coder_e1/model",
|
| 346 |
-
"explicit_exclude_patterns": [],
|
| 347 |
-
"explicit_include_patterns": [],
|
| 348 |
-
"intents": [
|
| 349 |
-
"uncertainty",
|
| 350 |
-
"validator_alignment",
|
| 351 |
-
"harness_alignment",
|
| 352 |
-
"runtime_protocol",
|
| 353 |
-
"session_continuity",
|
| 354 |
-
"worktree_discipline"
|
| 355 |
-
],
|
| 356 |
-
"prefer_qlora_for_backbone_bridge": true,
|
| 357 |
-
"product_line": "veriloop_coder",
|
| 358 |
-
"selection_mode": "minimal"
|
| 359 |
-
},
|
| 360 |
-
"selected_groups": [
|
| 361 |
-
{
|
| 362 |
-
"alpha": 16,
|
| 363 |
-
"dropout": 0.0,
|
| 364 |
-
"exclude_patterns": [
|
| 365 |
-
"(^|\\.)lm_head($|\\.)",
|
| 366 |
-
"(^|\\.)embed_tokens($|\\.)",
|
| 367 |
-
"(^|\\.)norm($|\\.)",
|
| 368 |
-
"attnres|attention_residual",
|
| 369 |
-
"dualpath",
|
| 370 |
-
"mhc|hyper[-_]?connection",
|
| 371 |
-
"rope|rotary",
|
| 372 |
-
"kvcache|kv_cache",
|
| 373 |
-
"(^|\\.)memory(_store|_bank)?($|\\.)"
|
| 374 |
-
],
|
| 375 |
-
"include_patterns": [
|
| 376 |
-
"(^|\\.)(uncertainty_head|uncertainty_adapter|calib(_head|ration_adapter)?)($|\\.)"
|
| 377 |
-
],
|
| 378 |
-
"intents": [
|
| 379 |
-
"uncertainty"
|
| 380 |
-
],
|
| 381 |
-
"layer_window": {
|
| 382 |
-
"mode": "all",
|
| 383 |
-
"value": 0.0
|
| 384 |
-
},
|
| 385 |
-
"matched_module_names": [
|
| 386 |
-
"uncertainty_head",
|
| 387 |
-
"uncertainty_head.calibration_mlp",
|
| 388 |
-
"uncertainty_head.proj"
|
| 389 |
-
],
|
| 390 |
-
"name": "group_1_custom_control_head",
|
| 391 |
-
"rank": 8,
|
| 392 |
-
"rationale": "Prefer explicit uncertainty / calibration heads over backbone surgery.",
|
| 393 |
-
"risk": "low",
|
| 394 |
-
"surface": "custom_control_head"
|
| 395 |
-
},
|
| 396 |
-
{
|
| 397 |
-
"alpha": 16,
|
| 398 |
-
"dropout": 0.05,
|
| 399 |
-
"exclude_patterns": [
|
| 400 |
-
"(^|\\.)lm_head($|\\.)",
|
| 401 |
-
"(^|\\.)embed_tokens($|\\.)",
|
| 402 |
-
"(^|\\.)norm($|\\.)",
|
| 403 |
-
"attnres|attention_residual",
|
| 404 |
-
"dualpath",
|
| 405 |
-
"mhc|hyper[-_]?connection",
|
| 406 |
-
"rope|rotary",
|
| 407 |
-
"kvcache|kv_cache",
|
| 408 |
-
"(^|\\.)memory(_store|_bank)?($|\\.)"
|
| 409 |
-
],
|
| 410 |
-
"include_patterns": [
|
| 411 |
-
"(^|\\.)(runtime_harness|query_runtime|task_brief|task_runtime|tool_protocol|permission_context|worktree|session_state|request_normalizer|action_allowlist|constraint_guard|progress_state|workspace_snapshot|repo_contract|knowledge_entry|completion_criteria|search_bridge|sandbox_search_bridge)(_adapter|_bridge|_head)?($|\\.)",
|
| 412 |
-
"(^|\\.)(toolspec|tool(_call)?(_grammar|_interface)?|harness|validator|rollback|receipt|patch|permission|session|worktree)(_adapter|_bridge|_head)?($|\\.)"
|
| 413 |
-
],
|
| 414 |
-
"intents": [
|
| 415 |
-
"harness_alignment",
|
| 416 |
-
"runtime_protocol",
|
| 417 |
-
"session_continuity",
|
| 418 |
-
"worktree_discipline"
|
| 419 |
-
],
|
| 420 |
-
"layer_window": {
|
| 421 |
-
"mode": "all",
|
| 422 |
-
"value": 0.0
|
| 423 |
-
},
|
| 424 |
-
"matched_module_names": [
|
| 425 |
-
"failure_signal_bridge.rollback_bridge",
|
| 426 |
-
"request_normalizer",
|
| 427 |
-
"request_normalizer.adapter",
|
| 428 |
-
"rollback_adapter",
|
| 429 |
-
"rollback_adapter.head",
|
| 430 |
-
"runtime_harness_adapter",
|
| 431 |
-
"runtime_harness_adapter.bridge",
|
| 432 |
-
"tool_protocol_adapter",
|
| 433 |
-
"tool_protocol_adapter.bridge",
|
| 434 |
-
"toolspec_bridge",
|
| 435 |
-
"toolspec_bridge.adapter",
|
| 436 |
-
"toolspec_head",
|
| 437 |
-
"toolspec_head.param_schema_adapter",
|
| 438 |
-
"toolspec_head.postcondition_adapter",
|
| 439 |
-
"toolspec_head.precondition_adapter",
|
| 440 |
-
"toolspec_head.receipt_formatter",
|
| 441 |
-
"toolspec_head.trigger_gate",
|
| 442 |
-
"toolspec_head.validator_gate",
|
| 443 |
-
"validator_feedback_loop.rollback_adapter"
|
| 444 |
-
],
|
| 445 |
-
"name": "group_2_custom_runtime_harness_bridge",
|
| 446 |
-
"rank": 8,
|
| 447 |
-
"rationale": "Runtime / harness obedience should attach to explicit interface bridges before any backbone fallback.",
|
| 448 |
-
"risk": "low",
|
| 449 |
-
"surface": "custom_runtime_harness_bridge"
|
| 450 |
-
},
|
| 451 |
-
{
|
| 452 |
-
"alpha": 16,
|
| 453 |
-
"dropout": 0.0,
|
| 454 |
-
"exclude_patterns": [
|
| 455 |
-
"(^|\\.)lm_head($|\\.)",
|
| 456 |
-
"(^|\\.)embed_tokens($|\\.)",
|
| 457 |
-
"(^|\\.)norm($|\\.)",
|
| 458 |
-
"attnres|attention_residual",
|
| 459 |
-
"dualpath",
|
| 460 |
-
"mhc|hyper[-_]?connection",
|
| 461 |
-
"rope|rotary",
|
| 462 |
-
"kvcache|kv_cache",
|
| 463 |
-
"(^|\\.)memory(_store|_bank)?($|\\.)"
|
| 464 |
-
],
|
| 465 |
-
"include_patterns": [
|
| 466 |
-
"(^|\\.)(validator_feedback|sandbox_result_validator|sandbox_rollback_bridge|failure_signal|rollback)(_adapter|_bridge|_head)?($|\\.)"
|
| 467 |
-
],
|
| 468 |
-
"intents": [
|
| 469 |
-
"validator_alignment"
|
| 470 |
-
],
|
| 471 |
-
"layer_window": {
|
| 472 |
-
"mode": "all",
|
| 473 |
-
"value": 0.0
|
| 474 |
-
},
|
| 475 |
-
"matched_module_names": [
|
| 476 |
-
"failure_signal_bridge",
|
| 477 |
-
"failure_signal_bridge.rollback_bridge",
|
| 478 |
-
"rollback_adapter",
|
| 479 |
-
"rollback_adapter.head",
|
| 480 |
-
"sandbox_rollback_bridge",
|
| 481 |
-
"sandbox_rollback_bridge.adapter",
|
| 482 |
-
"validator_feedback_bridge",
|
| 483 |
-
"validator_feedback_bridge.adapter",
|
| 484 |
-
"validator_feedback_loop.rollback_adapter"
|
| 485 |
-
],
|
| 486 |
-
"name": "group_3_custom_validator_bridge",
|
| 487 |
-
"rank": 8,
|
| 488 |
-
"rationale": "Validation and rollback fidelity should prefer explicit validator / rollback bridges.",
|
| 489 |
-
"risk": "low",
|
| 490 |
-
"surface": "custom_validator_bridge"
|
| 491 |
-
},
|
| 492 |
-
{
|
| 493 |
-
"alpha": 8,
|
| 494 |
-
"dropout": 0.0,
|
| 495 |
-
"exclude_patterns": [
|
| 496 |
-
"(^|\\.)lm_head($|\\.)",
|
| 497 |
-
"(^|\\.)embed_tokens($|\\.)",
|
| 498 |
-
"(^|\\.)norm($|\\.)",
|
| 499 |
-
"attnres|attention_residual",
|
| 500 |
-
"dualpath",
|
| 501 |
-
"mhc|hyper[-_]?connection",
|
| 502 |
-
"rope|rotary",
|
| 503 |
-
"kvcache|kv_cache",
|
| 504 |
-
"(^|\\.)memory(_store|_bank)?($|\\.)"
|
| 505 |
-
],
|
| 506 |
-
"include_patterns": [
|
| 507 |
-
"(^|\\.)(memory_boundary_guard|episodic_memory|session_compactor)(_adapter|_bridge|_head)?($|\\.)"
|
| 508 |
-
],
|
| 509 |
-
"intents": [
|
| 510 |
-
"session_continuity"
|
| 511 |
-
],
|
| 512 |
-
"layer_window": {
|
| 513 |
-
"mode": "all",
|
| 514 |
-
"value": 0.0
|
| 515 |
-
},
|
| 516 |
-
"matched_module_names": [
|
| 517 |
-
"episodic_memory",
|
| 518 |
-
"episodic_memory.adapter",
|
| 519 |
-
"memory_boundary_guard",
|
| 520 |
-
"memory_boundary_guard.adapter",
|
| 521 |
-
"memory_boundary_guard.rollback_filter",
|
| 522 |
-
"session_compactor",
|
| 523 |
-
"session_compactor.adapter"
|
| 524 |
-
],
|
| 525 |
-
"name": "group_4_custom_memory_boundary_bridge",
|
| 526 |
-
"rank": 4,
|
| 527 |
-
"rationale": "Session continuity should bind to boundary-aware memory packet surfaces rather than broad backbone tuning.",
|
| 528 |
-
"risk": "low",
|
| 529 |
-
"surface": "custom_memory_boundary_bridge"
|
| 530 |
-
}
|
| 531 |
-
],
|
| 532 |
-
"upstream_profile_hint": null,
|
| 533 |
-
"version": "veriloop.peft_target_selector.v2",
|
| 534 |
-
"warnings": []
|
| 535 |
-
},
|
| 536 |
-
"training_mode": "mounted_head",
|
| 537 |
-
"version": "veriloop.uncertainty_adapter_trainer.v5.qwen36",
|
| 538 |
-
"warnings": [
|
| 539 |
-
"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
|
| 540 |
-
"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op."
|
| 541 |
-
]
|
| 542 |
-
},
|
| 543 |
-
"status": "trained",
|
| 544 |
-
"train_metrics": {
|
| 545 |
-
"adapter_exported": true,
|
| 546 |
-
"auto_lora_from_ia3": false,
|
| 547 |
-
"best_epoch": 2,
|
| 548 |
-
"best_quality_score": 0.8625935807221907,
|
| 549 |
-
"epochs_completed": 4,
|
| 550 |
-
"loss": 0.009006613283418119,
|
| 551 |
-
"micro_batches": 154,
|
| 552 |
-
"micro_batches_total": 616,
|
| 553 |
-
"optimizer_steps": 10,
|
| 554 |
-
"optimizer_steps_total": 40,
|
| 555 |
-
"peft_method": "lora_narrow",
|
| 556 |
-
"used_peft": true
|
| 557 |
-
},
|
| 558 |
-
"version": "veriloop.uncertainty_adapter_trainer.v5.qwen36",
|
| 559 |
-
"warnings": [
|
| 560 |
-
"Harness Engineering is primary; PEFT is limited to obedience-facing, interface-facing support surfaces.",
|
| 561 |
-
"Backbone bridge tuning disabled explicitly; selector stays on custom surfaces or no-op.",
|
| 562 |
-
"Synthetic holdout split used for quality-oriented train/eval separation."
|
| 563 |
-
]
|
| 564 |
-
}
|
|
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