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
Upload README.md
Browse files
README.md
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tags:
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- veriloop
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---
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# VeriLoop Coder-E1
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**VeriLoop Coder-E1** is an open-weight coding model release built
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## Highlights
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VeriLoop Coder-E1 is
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- **Harness-ready coding behavior** —
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- **
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VeriLoop Coder-E1 should be
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| Model family | VeriLoop Coder-E1 |
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| Backbone | Qwen3.6-27B-compatible backbone |
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| Public release type | Open-weight backbone + four PEFT adapters |
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| Primary domain | Coding, software engineering, coding-agent workflows |
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| Languages | English, Chinese |
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| Weight format | `safetensors` |
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| Runtime target | Harness-driven coding systems, tool-mediated agents, repository workflows |
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| Public benchmark status | Formal benchmark results pending |
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The public release separates standard model assets from private production runtime infrastructure. Users can load the
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- Qwen3.6-27B-compatible backbone files in the repository root.
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- Standard sharded `safetensors` model weights.
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- Tokenizer, generation, and configuration files.
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- Four public PEFT
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- Public adapter README files, metric summaries, and adapter manifests.
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This separation is intentional
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| Rollback | `rollback_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Validator-aware repair behavior, rollback discipline, bounded revision control |
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| Evidence Binding | `evidence_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Stronger alignment between claims, evidence, provenance, and validation context |
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Each adapter is published independently.
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import torch
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repo_id = "veriloop-lab/veriloop-coder-e1"
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adapter_subfolder = "evidence_adapter/adapter" # choose one adapter
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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base_model = AutoModelForCausalLM.from_pretrained(
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- Repository understanding and patch drafting.
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- Validator-aware repair experiments.
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- Evidence-aware coding workflows.
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```
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tags:
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- veriloop
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- veriloop-coder
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- software-engineering
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- repository-understanding
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- tool-use
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- peft
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- lora
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- safetensors
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- harness-engineering
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- evidence-binding
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---
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# VeriLoop Coder-E1
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**VeriLoop Coder-E1** is an open-weight coding model release built for harness-ready software engineering workflows. It combines a Qwen3.6-27B-compatible backbone with four focused public PEFT adapters designed to shape coding-agent behavior around tool discipline, evidence awareness, rollback-safe revision, and uncertainty-calibrated decision signals.
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This repository is the **public standard release** of VeriLoop Coder-E1. It provides clean Hugging Face-compatible model artifacts for research, evaluation, and downstream experimentation while keeping private production runtime components, training data, and server-side orchestration logic out of the public package.
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> **Release status**
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>
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> This is the first public VeriLoop Coder-E1 27B release package. Formal benchmark results will be added after the dedicated evaluation run. Until then, this model card should be read as a release description and loading guide, not as a leaderboard claim.
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---
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## Highlights
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VeriLoop Coder-E1 is designed for coding-agent environments where a model must operate with repository context, tool calls, validation feedback, and iterative repair loops.
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- **Harness-ready coding behavior** — optimized for systems that coordinate model generation with tools, validators, execution feedback, and bounded repair loops.
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- **Tool-spec alignment** — improves response patterns around tool schemas, argument discipline, preconditions, postconditions, and execution-facing instruction formats.
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- **Evidence-bound coding style** — encourages tighter alignment between claims, code edits, validation signals, and supporting repository context.
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- **Rollback-aware revision behavior** — strengthens behavior around failed edits, validator negation, worktree-sensitive repair, and safe correction boundaries.
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- **Uncertainty-calibrated routing signals** — supports better control decisions around answer uncertainty, evidence gaps, execution necessity, specification mismatch, and risk pressure.
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- **Repository-scale workflow orientation** — intended for code understanding, patch drafting, debugging, refactoring assistance, and agentic software-engineering experiments.
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- **Standard open artifacts** — released with sharded `safetensors` backbone weights and PEFT-compatible adapter checkpoints.
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VeriLoop Coder-E1 should be understood as a **coding model foundation for harness-centric systems**. The full VeriLoop product experience may involve additional private runtime components such as tool orchestration, sandbox validation, evidence handling, memory, observability, and API-side routing.
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---
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|---|---|
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| Model family | VeriLoop Coder-E1 |
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| Backbone | Qwen3.6-27B-compatible backbone |
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| Public release type | Open-weight backbone + four public PEFT adapters |
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| Primary domain | Coding, software engineering, coding-agent workflows |
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| Languages | English, Chinese |
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| Weight format | `safetensors` |
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| Runtime target | Harness-driven coding systems, tool-mediated agents, repository workflows |
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| Public benchmark status | Formal benchmark results pending |
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The public release separates standard model assets from private production runtime infrastructure. Users can load the backbone directly, or mount one public PEFT adapter at a time for targeted experiments.
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---
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- Qwen3.6-27B-compatible backbone files in the repository root.
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- Standard sharded `safetensors` model weights.
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- Tokenizer, generation, and configuration files.
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- Four public PEFT adapter folders:
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- `toolspec_adapter/adapter`
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- `uncertainty_adapter/adapter`
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- `rollback_adapter/adapter`
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- `evidence_adapter/adapter`
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- Public adapter README files, metric summaries, and public adapter manifests.
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### Not Included
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- Internal logs, checkpoints, optimizer states, and scheduler states.
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- Private routing, sandbox, memory, evidence-gate, or production-serving logic.
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This separation is intentional: the repository provides standard open model assets, while production-grade coding-agent behavior may require a full runtime system around the model.
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---
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| Rollback | `rollback_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Validator-aware repair behavior, rollback discipline, bounded revision control |
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| Evidence Binding | `evidence_adapter/adapter` | `adapter_config.json`, `adapter_model.safetensors` | Stronger alignment between claims, evidence, provenance, and validation context |
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Each adapter is published independently. For standard PEFT loading, use one adapter at a time unless your runtime explicitly implements adapter composition or routing.
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---
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import torch
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repo_id = "veriloop-lab/veriloop-coder-e1"
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adapter_subfolder = "evidence_adapter/adapter" # choose one public adapter
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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base_model = AutoModelForCausalLM.from_pretrained(
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- Repository understanding and patch drafting.
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- Validator-aware repair experiments.
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- Evidence-aware coding workflows.
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- Uncertainty-aware software-engineering agents.
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- Harness and runtime policy research.
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---
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generation_config.json
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special_tokens_map.json
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toolspec_adapter/
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README.md
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metrics_summary.json
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veriloop_adapter_manifest.json
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adapter/
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README.md
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adapter_config.json
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adapter_model.safetensors
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uncertainty_adapter/
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README.md
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metrics_summary.json
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veriloop_adapter_manifest.json
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adapter/
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README.md
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adapter_config.json
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adapter_model.safetensors
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rollback_adapter/
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README.md
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metrics_summary.json
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veriloop_adapter_manifest.json
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adapter/
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README.md
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adapter_config.json
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adapter_model.safetensors
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evidence_adapter/
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README.md
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metrics_summary.json
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veriloop_adapter_manifest.json
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adapter/
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README.md
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adapter_config.json
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adapter_model.safetensors
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```
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---
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