--- library_name: transformers pipeline_tag: text-generation license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE base_model: - Qwen/Qwen3.6-27B base_model_relation: finetune language: - en - zh tags: - qwen3_6 - qwen3 - qwen - veriloop - veriloop-coder - code - coding-agent - agentic-coding - software-engineering - repository-understanding - tool-use - peft - lora - safetensors - transformers - harness-engineering - evidence-binding - rollback - uncertainty-calibration - long-context - open-weights - reasoning - conversational --- # VeriLoop Coder-E1 **VeriLoop Coder-E1** is an open-weight coding model release built on a Qwen3.6-27B backbone and specialized for harness-driven software engineering workflows. The public release provides a standard, Hugging Face-compatible model package: sharded `safetensors` backbone weights in the repository root, plus four focused PEFT adapters for coding-agent behavior shaping. It is designed for developers and researchers who want a coding model that is not only fluent at code generation, but also better prepared for tool-mediated execution, evidence-aware reasoning, rollback-safe revision, and uncertainty-calibrated coding workflows. > **Release note** > This is the first public VeriLoop Coder-E1 27B release package. Public benchmark results will be added after the formal evaluation run. The current repository focuses on releasing clean, standard model artifacts without exposing internal production Harness code, private runtime heads, training data, or server-side orchestration logic. --- ## Highlights VeriLoop Coder-E1 is built for coding-agent environments where a model must cooperate with external tools, validators, repository context, and iterative repair loops. - **Harness-ready coding behavior** — shaped for coding runtimes that use tool calls, validation feedback, staged execution, and bounded repair loops. - **Tool-spec alignment** — improves obedience to tool schemas, argument discipline, preconditions, postconditions, and execution-facing instruction formats. - **Evidence-bound coding style** — encourages stronger alignment between claims, code edits, validation signals, and supporting repository context. - **Rollback-aware revision behavior** — improves response patterns around failed edits, validator negation, worktree-sensitive repair, and safe correction boundaries. - **Uncertainty-calibrated routing signals** — supports more reliable decisions around answer uncertainty, evidence gaps, execution necessity, specification mismatch, and risk pressure. - **Repository-scale workflow orientation** — intended for code understanding, patch drafting, debugging, refactoring assistance, and agentic software engineering tasks. - **Open standard artifacts** — released with `safetensors` model shards and PEFT-compatible adapter checkpoints for public loading and reproducible experimentation. VeriLoop Coder-E1 should be viewed as a **coding model foundation for harness-centric systems**. The full VeriLoop product experience may involve additional runtime components such as tool orchestration, sandbox validation, evidence handling, memory, observability, and API-side routing. --- ## Model Overview | Property | Value | |---|---| | Model family | VeriLoop Coder-E1 | | Backbone | Qwen3.6-27B-compatible backbone | | Public release type | Open-weight backbone + four PEFT adapters | | Primary domain | Coding, software engineering, coding-agent workflows | | Languages | English, Chinese | | Weight format | `safetensors` | | Adapter format | PEFT / LoRA-style adapter checkpoints | | Runtime target | Harness-driven coding systems, tool-mediated agents, repository workflows | | Public benchmark status | Formal benchmark results pending | The public release separates standard model assets from private production runtime infrastructure. Users can load the base model directly, or mount one of the four public PEFT adapters for targeted experimentation. --- ## Public Release Contents ### Included - Qwen3.6-27B-compatible backbone files in the repository root. - Standard sharded `safetensors` model weights. - Tokenizer, generation, and configuration files. - Four public PEFT adapters: - `toolspec_adapter/adapter` - `uncertainty_adapter/adapter` - `rollback_adapter/adapter` - `evidence_adapter/adapter` - Public adapter README files, metric summaries, and adapter manifests. ### Not Included - Private runtime heads. - Internal Harness orchestration code. - Training JSONL files and evaluation JSONL files. - Internal logs, checkpoints, optimizer states, and scheduler states. - Private routing, sandbox, memory, evidence-gate, or production-serving logic. This separation is intentional. The public repository provides standard open model assets, while production-grade coding-agent behavior may require a full runtime system around the model. --- ## Adapter Overview | Adapter | Folder | Public files | Role | |---|---|---|---| | ToolSpec | `toolspec_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Tool-call discipline, schema obedience, precondition/postcondition sensitivity | | Uncertainty | `uncertainty_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Runtime uncertainty calibration across answer, evidence, execution, specification, and risk signals | | Rollback | `rollback_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Validator-aware repair behavior, rollback discipline, bounded revision control | | Evidence Binding | `evidence_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Stronger alignment between claims, evidence, provenance, and validation context | Each adapter is published independently. Users can load one adapter at a time for focused evaluation, or build their own adapter-selection policy around the public artifacts. --- ## Quickstart ### Install ```bash pip install -U transformers peft accelerate safetensors ``` ### Load the Backbone ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch repo_id = "veriloop-lab/veriloop-coder-e1" tokenizer = AutoTokenizer.from_pretrained( repo_id, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( repo_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ) model.eval() ``` ### Load One Public PEFT Adapter ```python from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel import torch repo_id = "veriloop-lab/veriloop-coder-e1" adapter_subfolder = "evidence_adapter/adapter" # choose one adapter tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True) base_model = AutoModelForCausalLM.from_pretrained( repo_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ) model = PeftModel.from_pretrained( base_model, repo_id, subfolder=adapter_subfolder, ) model.eval() ``` Available adapter subfolders: ```text toolspec_adapter/adapter uncertainty_adapter/adapter rollback_adapter/adapter evidence_adapter/adapter ``` ### Minimal Generation Example ```python prompt = "Write a Python function that validates whether a patch should be accepted after unit tests." messages = [ {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=1024, temperature=0.6, top_p=0.95, do_sample=True, ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` --- ## Serving Notes The repository root contains the backbone model files and can be served with standard inference engines that support the underlying architecture. PEFT adapters may require framework-specific LoRA loading support. ### vLLM Backbone Serving ```bash vllm serve veriloop-lab/veriloop-coder-e1 \ --trust-remote-code \ --tensor-parallel-size 2 \ --max-model-len 131072 ``` For public PEFT adapters, use the serving engine's LoRA/adapter loading mechanism if supported by your deployment configuration. The full VeriLoop production setup may use additional private runtime components that are not part of this public release. --- ## Recommended Use Cases VeriLoop Coder-E1 is intended for research and development in: - Coding-agent model evaluation. - Tool-mediated code generation. - Repository understanding and patch drafting. - Validator-aware repair experiments. - Evidence-aware coding workflows. - Uncertainty-aware software engineering agents. - Harness and runtime policy research. --- ## Limitations - Public benchmark numbers are not yet included in this release and will be added after formal evaluation. - The public repository does not include private runtime heads or internal Harness orchestration. - Public adapter loading does not reproduce the complete VeriLoop production API behavior. - Long-context and high-throughput serving require appropriate GPU memory, KV-cache planning, and inference-engine configuration. - Users should validate generated code with tests, static analysis, sandboxing, and security review before deployment. --- ## Safety and Responsible Use VeriLoop Coder-E1 is a coding-focused model and may produce incorrect, insecure, incomplete, or environment-specific code. Users are responsible for validating outputs before use. Recommended safeguards include: - Run generated code in isolated environments. - Review dependencies and shell commands before execution. - Use automated tests and linters. - Treat security-sensitive code paths as high risk. - Avoid using generated code for destructive actions without human review. --- ## File Layout ```text README.md config.json configuration.json model.safetensors.index.json veriloop-coder-e1-model-00001-of-00010.safetensors ... veriloop-coder-e1-model-00010-of-00010.safetensors tokenizer.json tokenizer_config.json generation_config.json special_tokens_map.json toolspec_adapter/ README.md metrics_summary.json veriloop_adapter_manifest.json adapter/ README.md adapter_config.json adapter_model.safetensors uncertainty_adapter/ README.md metrics_summary.json veriloop_adapter_manifest.json adapter/ README.md adapter_config.json adapter_model.safetensors rollback_adapter/ README.md metrics_summary.json veriloop_adapter_manifest.json adapter/ README.md adapter_config.json adapter_model.safetensors evidence_adapter/ README.md metrics_summary.json veriloop_adapter_manifest.json adapter/ README.md adapter_config.json adapter_model.safetensors ``` --- ## Evaluation Status Formal benchmark results are planned. Future updates may include coding-agent benchmarks, repository-level tasks, tool-use evaluations, validation/rollback tests, and long-context software-engineering workflows. Until benchmark numbers are published, this model card should be interpreted as a release description and loading guide, not as a performance leaderboard claim. --- ## Citation If you use VeriLoop Coder-E1 in research, prototypes, or agent systems, please cite: ```bibtex @misc{veriloop_coder_e1_2026, title = {VeriLoop Coder-E1: Harness-Ready Open-Weight Coding Model Release}, author = {VeriLoop Lab}, year = {2026}, howpublished = {Hugging Face model repository}, url = {https://huggingface.co/veriloop-lab/veriloop-coder-e1} } ``` --- ## Acknowledgements VeriLoop Coder-E1 is built on top of the Qwen3.6-27B open-weight backbone. We thank the open-source model and tooling communities for enabling reproducible model development, adapter-based experimentation, and open deployment workflows.