Instructions to use tsinghua-sigs-robot-lab/VeriLoop-E2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsinghua-sigs-robot-lab/VeriLoop-E2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tsinghua-sigs-robot-lab/VeriLoop-E2")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tsinghua-sigs-robot-lab/VeriLoop-E2")
model = AutoModelForMultimodalLM.from_pretrained("tsinghua-sigs-robot-lab/VeriLoop-E2", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tsinghua-sigs-robot-lab/VeriLoop-E2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsinghua-sigs-robot-lab/VeriLoop-E2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsinghua-sigs-robot-lab/VeriLoop-E2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsinghua-sigs-robot-lab/VeriLoop-E2

SGLang

How to use tsinghua-sigs-robot-lab/VeriLoop-E2 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 "tsinghua-sigs-robot-lab/VeriLoop-E2" \
    --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": "tsinghua-sigs-robot-lab/VeriLoop-E2",
		"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 "tsinghua-sigs-robot-lab/VeriLoop-E2" \
        --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": "tsinghua-sigs-robot-lab/VeriLoop-E2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsinghua-sigs-robot-lab/VeriLoop-E2 with Docker Model Runner:
```
docker model run hf.co/tsinghua-sigs-robot-lab/VeriLoop-E2
```

ConorWang commited on 12 days ago

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@@ -1,3 +1,423 @@
 ---
 license: apache-2.0
 ---

 ---
+library_name: transformers
+pipeline_tag: text-generation
 license: apache-2.0
+base_model:
+  - Qwen/Qwen3.8-27B
+base_model_relation: finetune
+language:
+  - en
+  - zh
+tags:
+  - veriloop
+  - post-training
+  - coding-agent
+  - software-engineering
+  - mathematical-reasoning
+  - scientific-reasoning
+  - tool-use
+  - long-context
+  - safetensors
+  - vllm
+  - apache-2.0
 ---
+<div align="center">
+<img src="https://huggingface.co/tsinghua-sigs-robot-lab/veriloop-coder-e1/resolve/main/veriloop_logo.png" width="154" alt="VeriLoop logo">
+# VeriLoop E2
+### Open 27B Post-Trained Model for Code Agents, Mathematical Reasoning, and Scientific Problem Solving
+**Built on Qwen3.8-27B · 262K native context · Apache License 2.0**
+**Developed by Tsinghua SIGS Robot Lab · Libo Wang**
+<p>
+  <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache--2.0-2F80ED?style=flat-square" alt="License: Apache 2.0"></a>
+  <img src="https://img.shields.io/badge/Base-Qwen3.8--27B-5B5BD6?style=flat-square" alt="Base: Qwen3.8-27B">
+  <img src="https://img.shields.io/badge/Context-262K-1F6FEB?style=flat-square" alt="Native context: 262K">
+  <img src="https://img.shields.io/badge/Serving-vLLM%200.17.0-0A7F6F?style=flat-square" alt="Serving: vLLM 0.17.0">
+  <img src="https://img.shields.io/badge/Stage-Post--Training-7C3AED?style=flat-square" alt="Stage: Post-Training">
+</p>
+<p>
+  <a href="https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2"><strong>Model</strong></a> ·
+  <a href="xxxx"><strong>Technical Report</strong></a> ·
+  <a href="https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence"><strong>Evaluation Evidence</strong></a> ·
+  <a href="xxxx"><strong>Scientific Artifacts</strong></a> ·
+  <a href="xxxx"><strong>GitHub</strong></a> ·
+  <a href="xxxx"><strong>Zenodo</strong></a>
+</p>
+</div>
+---
+## Overview
+**VeriLoop E2** is an open 27B post-trained model built on **Qwen3.8-27B**. It is designed as the model component of the broader VeriLoop reasoning system, with emphasis on three workloads: **software-engineering agents, mathematical reasoning, and scientific problem solving**.
+The system separates generative intelligence from verification authority. **VeriLoop E2** is responsible for proposal generation, abstraction, diagnosis, repair hypotheses, and structured reasoning. The **VeriLoop Harness** governs evidence admission, deterministic checks, external verification, commit/rollback, stopping, and evidence-state persistence. The model therefore does not self-certify its own progress.
+This release focuses on the model weights, public inference path, evaluation record, and public functional description of the Harness. The production Harness implementation itself is **not included** in this repository.
+### Release highlights
+- **27B open-weight post-trained model** derived from Qwen3.8-27B.
+- **262,144-token native context window** in the released tokenizer configuration.
+- Strong release results across nine code-agent, mathematics, and science benchmarks, including **76.2% SWE-bench Pro**, **88.8% Terminal-Bench 2.1**, **98.3% AIME 2026**, **93.9% GPQA Diamond**, and **89.6% Apex 2025**.
+- A reproducible scientific-reasoning program built around verifier-governed recurrence rather than unconstrained retry.
+- Two public-facing scientific demonstrations: a strict finite-dimensional **Riemann ζ zero-proportion certificate at 67.350003708785593%**, and **Asymptotic Graviton Tomography** for the black-hole information problem.
+- OpenAI-compatible serving through **vLLM 0.17.0** with a validated 131,072-token serving configuration.
+---
+## Model Summary
+| Property | VeriLoop E2 |
+|---|---|
+| Model family | VeriLoop E2 |
+| Base model | Qwen3.8-27B |
+| Parameter class | 27B |
+| HF architecture class | `Qwen3_5ForConditionalGeneration` |
+| Training stage | Post-Training |
+| Primary domains | Code agents, software engineering, mathematics, scientific reasoning |
+| Public post-training corpus accounting | 1,841,831 records |
+| Native context length | 262,144 tokens |
+| Validated vLLM serving length | 131,072 tokens |
+| Tokenizer class | `Qwen2Tokenizer` |
+| Weight format | `safetensors` |
+| Languages | English, Chinese |
+| Recommended serving engine | vLLM 0.17.0 |
+| Model-weight license | Apache License 2.0 |
+| Release year | 2026 |
+The post-training mix spans repository-level software engineering, terminal and tool use, mathematical reasoning, scientific reasoning, verifier-sensitive repair, and recurrence-oriented training. Exact data construction, filtering, and training methodology are documented in the technical report rather than duplicated here.
+---
+## Benchmark Results
+The README reports the **frozen release scores** for VeriLoop E2. Agentic benchmarks use the E2 checkpoint inside the frozen evaluation workflow, including the internal VeriLoop Harness where required by the task, benchmark-native tools, and the benchmark's official or designated evaluator. Exact per-benchmark protocols, task-level outputs, evaluator receipts, and integrity metadata are published separately in the **Evaluation Evidence** package.
+> **Attribution boundary.** The reported results characterize the evaluated E2 system configuration. They should not be interpreted as evidence that an untouched Qwen3.8-27B base checkpoint, or the E2 checkpoint outside the evaluated runtime, reproduces the same numbers.
+<p align="center">
+  <img src="assets/veriloop_e2_benchmark_grid.png" width="100%" alt="VeriLoop E2 benchmark comparison across nine public benchmarks">
+</p>
+<p align="center">
+  <sub><strong>Figure 1.</strong> VeriLoop E2 release snapshot across nine public benchmarks. Higher is better. Provider colors are fixed across panels; exact public model variants are shown in the comparison tables below. Full protocol and source provenance: <a href="https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence">Evaluation Evidence</a>.</sub>
+</p>
+### Code and agentic benchmarks
+| Benchmark | **VeriLoop E2** | OpenAI | Anthropic | Kimi | GLM | Qwen | DeepSeek |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **SWE-bench Pro** | **[76.2](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/swe-bench-pro)** | GPT-5.6 Sol 64.6 | Claude Fable 5.1 81.2 | — | GLM-5.2 Max 62.1 | Qwen3.8-Max 67.7 | DeepSeek V4 Pro Max 55.4 |
+| **Terminal-Bench 2.1** | **[88.8](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-2.1)** | GPT-5.6 Sol 88.8 | — | Kimi K3 88.3 | GLM-5.3 88.2 | Qwen3.8-Max 86.6 | DeepSeek V4 Pro 87.9 |
+| **DeepSWE v1.1** | **[64.6](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/deepswe-1.1)** | GPT-5.6 Sol 72.7 | Claude Fable 5 69.7 | Kimi K3 67.5 | GLM-5.3 66.9 | — | DeepSeek V4 Pro 62.7 |
+| **Terminal-Bench 3.0** | **[29.7](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-3.0)** | GPT-5.6 Sol 34.6 | Claude Fable 5 33.7 | Kimi K3 17.4 | GLM-5.3 28.3 | — | — |
+| **Terminal-Bench 4.0** | **[37.9](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-4.0)** | GPT-6 Astra 59.6 | Claude Fable 5.1 55.1 | — | GLM-5.3 41.8 | — | — |
+| **SWE-Marathon v1.1** | **45.0** | GPT-5.6 Sol 42.5 | Claude Opus 4.8 48.8 | Kimi K3 48.1 | GLM-5.3 42.5 | — | — |
+### Mathematics and science benchmarks
+| Benchmark | **VeriLoop E2** | OpenAI | Anthropic | Kimi | GLM | DeepSeek | Gemini |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **AIME 2026** | **[98.3](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/aime-2026)** | GPT-5.5 100.0 | Claude Opus 4.8 100.0 | Kimi K3 97.0 | — | DeepSeek V4 Pro 97.0 | Gemini 3.1 Pro 98.0 |
+| **GPQA Diamond** | **[93.9](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/gpqa-diamond)** | GPT-5.6 Sol 94.1 | Claude Fable 5 92.6 | Kimi K3 93.5 | GLM-5.2 Max 91.2 | — | — |
+| **Apex 2025** | **[89.6](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/apex-2025)** | GPT-5.5 80.0 | Claude Opus 4.8 81.0 | Kimi K3 66.0 | — | DeepSeek V4 Pro 28.0 | Gemini 3.1 Pro 61.0 |
+A dash means that the release figure does not include a public comparison point for that provider on that benchmark. Each linked VeriLoop E2 score above resolves directly to its benchmark-specific public evidence directory. External reference values mirror the frozen comparison set used in Figure 1; harness notes and protocol caveats are retained in the evaluation ledger rather than duplicated here. **SWE-Marathon v1.1 (45.0)** remains reported on the model page, but is intentionally not represented by a Hugging Face Native Benchmark `.eval_results` entry because a stable HF benchmark registration/task identifier is not currently available.
+### Evaluation evidence
+The public evidence package is intended to make the benchmark record inspectable rather than merely declarative. Where available, each task record binds:
+```text
+task identity
+    ↓
+model / system output
+    ↓
+benchmark-native execution or evaluator record
+    ↓
+score / pass-fail decision
+    ↓
+integrity metadata and provenance
+```
+**Evidence repository:** [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence)
+| Benchmark | Public evaluation source |
+|---|---|
+| SWE-bench Pro | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/swe-bench-pro](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/swe-bench-pro) |
+| Terminal-Bench 2.1 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-2.1](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-2.1) |
+| DeepSWE v1.1 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/deepswe-1.1](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/deepswe-1.1) |
+| Terminal-Bench 3.0 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-3.0](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-3.0) |
+| Terminal-Bench 4.0 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-4.0](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/terminal-bench-4.0) |
+| AIME 2026 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/aime-2026](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/aime-2026) |
+| GPQA Diamond | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/gpqa-diamond](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/gpqa-diamond) |
+| Apex 2025 | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/apex-2025](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence/tree/main/apex-2025) |
+The structured Hugging Face evaluation descriptors for these eight registered benchmarks are published in the model repository under [`/.eval_results/`](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2/tree/main/.eval_results).
+---
+## VeriLoop Harness
+VeriLoop is not designed around the idea that a model should announce its own improvement. The Harness treats model output as a **candidate state** that must earn admission through external evidence.
+The current public abstraction is **VeriLoop-Governed Recurrence (VGR)**:
+```text
+Current request
+    ↓
+Contract compilation
+    ↓
+VeriLoop E2 proposes a candidate
+    ↓
+External / deterministic verification
+    ↓
+Protected evidence state comparison
+    ├── no protected regression + at least one strict improvement → COMMIT
+    ├── otherwise                                             → ROLLBACK
+    └── zero-rank certificate                                → STOP
+    ↓
+Verified evidence becomes the next recurrence state
+```
+The key boundary is deliberate:
+- **Model authority:** propose, reason, abstract, diagnose, synthesize, repair.
+- **Harness authority:** admit evidence, execute deterministic checks, verify, commit, roll back, stop, and persist verified state.
+- **Benchmark / domain authority:** define task truth through native evaluators, tests, formal checks, numerical certificates, or other domain-specific validators.
+This architecture is intended to preserve capability while preventing self-reported success from becoming system state. In software engineering, that means tests and execution receipts dominate plausible-looking patches. In mathematics and physics, it means a retained derivation must survive the relevant symbolic, numerical, or formal checks before it is promoted.
+The production implementation contains private orchestration, routing, thresholds, prompt compilation, evidence-state machinery, repair arbitration, and deployment controls. Those implementation details are not part of this open model release. The README exposes the **functional contract**, not the proprietary runtime.
+---
+## Public 14-Rule Engineering Contract
+The public Golden Rules are the model-visible execution discipline used to keep long-horizon work bounded, testable, and auditable.
+| # | Rule | Public meaning |
+|---:|---|---|
+| 1 | **Current Request Supremacy** | The current request and exact output contract override stale memory, templates, and unrelated context. |
+| 2 | **Evidence Before Escalation** | Search, tools, reverse analysis, or repair are triggered by concrete missing evidence or observed failure, not instinct. |
+| 3 | **Read Before Rewrite** | Inspect the relevant entry points, interfaces, tests, conventions, and failure signals before editing. |
+| 4 | **Minimal Sufficient Implementation** | Produce the smallest complete artifact that satisfies the task and preserves required interfaces. |
+| 5 | **Surgical Repair, Not Blind Regeneration** | Repair the broken invariant; broaden the rewrite only when evidence shows local repair is insufficient. |
+| 6 | **Intent Tests Beat Cosmetic Tests** | Syntax and formatting matter, but functional intent is the decisive acceptance criterion. |
+| 7 | **Fail Loud, Never Fake Success** | Unknowns, skipped checks, degraded states, and failures remain explicit; unexecuted validation is never reported as success. |
+| 8 | **Deterministic Logic Belongs in Code** | Parsing, scoring, structural checks, transformations, and reproducible validation should be deterministic whenever possible. |
+| 9 | **Budget Is a First-Class Contract** | Token, tool, time, and compute budgets are part of the task contract rather than afterthoughts. |
+| 10 | **Tool Use Must Be Typed and Accountable** | Every tool action has a trigger, expected output, and defined downstream consumer. |
+| 11 | **Checkpoint Long Tasks** | Persist useful candidate state, validation receipts, repair records, and progress across long-running work. |
+| 12 | **Follow Local Conventions** | Respect repository, benchmark, filename, API, language, and artifact conventions unless the request explicitly changes them. |
+| 13 | **One Final Deliverable, Full Evidence Bundle** | Select one deliverable while retaining the evidence needed to explain and reproduce why it was selected. |
+| 14 | **Separate Core Discipline from Domain Overlays** | Domain- or benchmark-specific rules apply only when relevant and cannot override the current request. |
+---
+## Scientific Demonstrations
+The scientific demonstrations are not presented as isolated chat transcripts. They are examples of how the E2 model and the internal Harness can divide a difficult research problem into candidate derivations, falsifiable subclaims, executable checks, and retained evidence.
+### Riemann ζ: 67.350003708785593% strict finite-dimensional certificate
+The released Riemann artifact reports a frozen assembly value of
+\[
+\kappa = 67.350003708785593\%.
+\]
+The current strict package closes **3/3 local inequalities**, resolves **327/327 difficult wells**, executes **190,375,830 strict branch-and-bound nodes**, and passes the final **exact rational assembly** check.
+What this result **does** establish within the released artifact is a strict finite-dimensional computer-assisted certificate under its stated analytic setup and imported assumptions. What it **does not** establish is equally important:
+- it is **not a proof of the Riemann Hypothesis**;
+- it is **not yet an end-to-end Lean/nanoda kernel proof** of the complete upstream analytic chain;
+- imported analytic normalization steps must remain clearly separated from the finite-dimensional certificate until the formal bridge and complete replay are closed.
+The public package therefore emphasizes claim discipline, reproducibility, and certificate structure rather than treating a numerical percentage as a substitute for mathematical provenance.
+**Artifact:** `xxxx` · **Technical note:** `xxxx` · **Zenodo:** `xxxx`
+### Black-hole information problem: Asymptotic Graviton Tomography
+**Asymptotic Graviton Tomography** is the second scientific reasoning demonstration. It studies an information-reconstruction route through asymptotic gravitational observables, using the Harness to separate retained derivations from rejected or insufficiently supported branches.
+The public claim is intentionally bounded: this is a **research demonstration of a verifier-governed theoretical-physics derivation**, not a declaration that the black-hole information paradox has been solved. The artifact is intended to expose the derivation structure, assumptions, checks, and remaining theoretical boundaries clearly enough for external scientific criticism.
+**Artifact:** `xxxx` · **Technical note:** `xxxx`
+> The Riemann and black-hole demo artifacts are released separately under **research-only, non-commercial terms**. They are not covered by the Apache-2.0 grant for the model weights and public inference utilities unless a specific file explicitly says otherwise.
+---
+## Inference
+### Recommended environment
+The following stack is the validated reference environment for the public serving path:
+| Component | Version / setting |
+|---|---|
+| Python | 3.12.x |
+| vLLM | 0.17.0 |
+| PyTorch | 2.10.0 + CUDA 12.9 build |
+| CUDA runtime | 12.9 |
+| Transformers | 4.57.6 |
+| Triton | 3.6.0 |
+| Dtype | `bfloat16` |
+| Validated serving context | 131,072 tokens |
+The released tokenizer advertises a native maximum length of **262,144 tokens**. The 131,072-token value above is the **validated public serving configuration**, not a redefinition of the model's native context length. Longer serving windows require appropriate accelerator memory and KV-cache planning.
+### vLLM server
+Use the tokenizer and chat template shipped with the model repository.
+```bash
+python -m pip install "vllm==0.17.0"
+MODEL="<MODEL_PATH_OR_HF_ID>"
+vllm serve "${MODEL}" \
+  --served-model-name veriloop-e2 \
+  --dtype bfloat16 \
+  --model-impl vllm \
+  --language-model-only \
+  --max-model-len 131072 \
+  --max-num-seqs 16 \
+  --gpu-memory-utilization 0.92 \
+  --generation-config vllm \
+  --disable-uvicorn-access-log \
+  --host 127.0.0.1 \
+  --port 8001
+```
+### OpenAI-compatible request
+```bash
+curl http://127.0.0.1:8001/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "veriloop-e2",
+    "messages": [
+      {"role": "user", "content": "Reply exactly: 8001_OK"}
+    ],
+    "temperature": 0,
+    "max_tokens": 16,
+    "stream": false
+  }'
+```
+A validated smoke test returns:
+```text
+8001_OK
+```
+### Python client
+```python
+from openai import OpenAI
+client = OpenAI(
+    base_url="http://127.0.0.1:8001/v1",
+    api_key="EMPTY",
+)
+response = client.chat.completions.create(
+    model="veriloop-e2",
+    messages=[
+        {"role": "user", "content": "Explain why rollback matters in verifier-governed reasoning."}
+    ],
+    temperature=0.2,
+    max_tokens=1024,
+)
+print(response.choices[0].message.content)
+```
+### Protocol note
+For tool use or long multi-turn reasoning, treat the repository-shipped tokenizer and chat template as part of the model protocol. Replacing role delimiters, tool-call syntax, stop semantics, or reasoning-history behavior can change observed system behavior even when the weights are unchanged.
+---
+## Release Boundary and Artifact Terms
+Different artifacts intentionally carry different permissions. Do not infer that the model-weight license automatically applies to separately published scientific artifacts or private system components.
+| Artifact | Public status | Terms |
+|---|---|---|
+| **VeriLoop E2 model weights, tokenizer, configuration** | Public | **Apache License 2.0** |
+| **Public vLLM launch / inference utilities** | Public | **Apache License 2.0** |
+| **Benchmark results and evaluation evidence** | Public / separately published | Reuse permitted with attribution to **VeriLoop E2 / Libo Wang**; upstream benchmark assets retain their original terms |
+| **Riemann ζ scientific artifact** | Public / separately published | **Research-only, non-commercial**; see artifact-specific terms |
+| **Asymptotic Graviton Tomography artifact** | Public / separately published | **Research-only, non-commercial**; see artifact-specific terms |
+| **Production VeriLoop Harness implementation** | Not included | Not licensed by this release |
+The open model license does not disclose or license unpublished Harness orchestration, prompt compilation, verifier routing, private evidence-state schemas, repair arbitration, deployment infrastructure, private training data, or other non-distributed internal systems.
+---
+## Limitations
+- VeriLoop E2 is a post-trained model component; the complete production VeriLoop Harness is not part of this release.
+- Reported system benchmarks may depend on benchmark-native tools, sandbox behavior, evaluator versions, and the frozen Harness configuration described in the evidence package.
+- The model can still produce incorrect code, invalid proofs, physically unsupported arguments, insecure commands, or incomplete analyses.
+- A plausible-looking derivation is not equivalent to a verified result; domain-native verification remains necessary.
+- Long-context performance depends on serving configuration, accelerator memory, KV-cache budget, and workload shape.
+- Community-modified templates, stop rules, parsers, or client logic can materially change observed tool-use and reasoning behavior.
+- Scientific demonstration artifacts have explicit scope boundaries and should not be generalized beyond the claims actually certified by their released evidence.
+---
+## Links
+| Resource | Link |
+|---|---|
+| Hugging Face model | [https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2) |
+| Technical report | `xxxx` |
+| Evaluation evidence | [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence) |
+| GitHub | `xxxx` |
+| Riemann ζ artifact | `xxxx` |
+| Black-hole / Asymptotic Graviton Tomography artifact | `xxxx` |
+| Zenodo DOI | `xxxx` |
+Unresolved links in this table remain placeholders until their corresponding public artifacts are released. The Hugging Face model and evaluation-evidence links above are final public identifiers.
+---
+## Citation
+If you use VeriLoop E2 in research, please cite the model release and the relevant evaluation or scientific artifact separately.
+```bibtex
+@misc{wang2026veriloope2,
+  title        = {VeriLoop E2: A 27B Post-Trained Model for Code, Mathematics, and Scientific Reasoning},
+  author       = {Wang, Libo},
+  year         = {2026},
+  note         = {Tsinghua Shenzhen International Graduate School (SIGS)},
+  howpublished = {Open model release},
+  url          = {https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2}
+}
+```
+For benchmark figures or evaluation records, attribution should identify **VeriLoop E2 / Libo Wang** and link to the public evaluation evidence package: [https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence](https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence).
+---
+## Acknowledgements
+VeriLoop E2 builds on **Qwen3.8-27B** and the broader open-source model-serving, evaluation, and scientific-computing ecosystem. We thank the communities behind Qwen, Transformers, vLLM, Safetensors, software-engineering benchmarks, mathematical evaluation suites, and reproducible scientific computation.
+The model, benchmark evidence, and scientific artifacts are published with explicit boundaries so that capability claims can be inspected at the level at which they were actually produced and verified.