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
File size: 8,734 Bytes
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"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."
]
} |