Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
veriloop
auto-round
community-quantization
experimental
conversational
4-bit precision
Instructions to use bowmanslayer/VeriLoop-E2-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bowmanslayer/VeriLoop-E2-W4A16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bowmanslayer/VeriLoop-E2-W4A16") 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("bowmanslayer/VeriLoop-E2-W4A16") model = AutoModelForMultimodalLM.from_pretrained("bowmanslayer/VeriLoop-E2-W4A16", 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bowmanslayer/VeriLoop-E2-W4A16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bowmanslayer/VeriLoop-E2-W4A16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bowmanslayer/VeriLoop-E2-W4A16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bowmanslayer/VeriLoop-E2-W4A16
- SGLang
How to use bowmanslayer/VeriLoop-E2-W4A16 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 "bowmanslayer/VeriLoop-E2-W4A16" \ --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": "bowmanslayer/VeriLoop-E2-W4A16", "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 "bowmanslayer/VeriLoop-E2-W4A16" \ --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": "bowmanslayer/VeriLoop-E2-W4A16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bowmanslayer/VeriLoop-E2-W4A16 with Docker Model Runner:
docker model run hf.co/bowmanslayer/VeriLoop-E2-W4A16
Release unmodified quantization with complete saved evaluation metrics and historical reference
711ed9f verified |
Download README.md from bowmanslayer/VeriLoop-E2-W4A16: direct link, hf CLI and curl.
- Browser
- Download file 5.1 kB
-
https://huggingface.co/bowmanslayer/VeriLoop-E2-W4A16/resolve/main/README.md
- Command line
-
hf download hf://bowmanslayer/VeriLoop-E2-W4A16/README.md
-
curl -L -o README.md https://huggingface.co/bowmanslayer/VeriLoop-E2-W4A16/resolve/main/README.md
5.1 kB
| license: apache-2.0 | |
| base_model: tsinghua-sigs-robot-lab/VeriLoop-E2 | |
| base_model_relation: quantized | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - veriloop | |
| - auto-round | |
| - community-quantization | |
| - experimental | |
| # VeriLoop-E2-W4A16 | |
| Community quantization of [tsinghua-sigs-robot-lab/VeriLoop-E2](https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2/tree/9379d199adcc44acdf836558037163878e3a37ae), fixed revision `9379d199adcc44acdf836558037163878e3a37ae`. This is not an official Tsinghua release. Model architecture is `Qwen3_5ForConditionalGeneration`; the upstream names the base Qwen3.8-27B. | |
| ## What is included | |
| AutoRound W4A16, symmetric group 128, 256 public Pile calibration samples, 512 tokens, 200 iterations, batch 4. Eligible weights are INT4; small hybrid projections, activations and retained weights are BF16. All source tensors are represented; the 333 vision tensors are preserved exactly. | |
| No abliteration was applied to this version. | |
| ## Local paired evaluation | |
| Same frozen 1,514 questions, seed 20260816, thinking enabled with the original template and publisher raw-content protocol, greedy decoding, max output 32,768 tokens. These are our legacy sampled/custom scorers, **not official leaderboard results**. IFEval uses a partial custom rule implementation. See EVALUATION.md and evaluation.json for details and validity flags. | |
| | Task | n | Original w4a16 | Ablated w4a16 | Delta pp | | |
| |---|---:|---:|---:|---:| | |
| | mmlu | 150 | 84.00% | 76.00% | -8.00 | | |
| | cmmlu | 150 | 84.67% | 75.33% | -9.33 | | |
| | mmlu_pro | 150 | 78.67% | 70.67% | -8.00 | | |
| | ceval | 150 | 84.00% | 76.00% | -8.00 | | |
| | arc | 150 | 80.67% | 85.33% | +4.67 | | |
| | truthfulqa | 150 | 58.67% | 71.33% | +12.67 | | |
| | gsm8k | 100 | 95.00% | 88.00% | -7.00 | | |
| | math500 | 100 | 75.00% | 73.00% | -2.00 | | |
| | bbh | 150 | 75.33% | 65.33% | -10.00 | | |
| | humaneval | 164 | 89.63% | 89.63% | +0.00 | | |
| | ifeval | 100 | 76.00% | 69.00% | -7.00 | | |
| Overall original → ablated: **80.05% → 76.42%**, difference -3.63 pp; approximate paired 95% CI [-5.894, -1.371] pp. Empty responses: [9, 8]; length-limited responses: [9, 8]. Both stay in the denominator. Empty-rate validity gate: False. | |
| On 100 separate refusal probes (thinking disabled, 2048-token budget), opening-30-word marker counts were **98/100 → 0/100**. Marker absence is not proof of semantic compliance. Truncation/budget hits: [1, 9]; validity: [True, True]. The first 16 probes overlap candidate selection; see the 84-item held-out subset in evaluation.json when available. | |
| ## Loading | |
| ```bash | |
| vllm serve /path/to/VeriLoop-E2-W4A16 --tensor-parallel-size 2 --generation-config vllm | |
| ``` | |
| The measured runtime was vLLM 0.29.0 with a local FA2/FP8-KV plugin on dual RTX 3090. That local setup requires its plugin-specific library environment variables; this repository does not bundle the plugin. The command above is a starting point for a compatible installed runtime, not a tested stock-vLLM support matrix. No remote custom model code is required. Vision weights are retained exactly, but visual inference is not validated in this evaluation. | |
| ## Scope and limitations | |
| Use the repository-shipped template and publisher raw-content serving protocol. Do not add a Qwen3 reasoning parser: some completed direct answers omit a closing think tag and that parser classifies them entirely as hidden reasoning. Removing it recovered all 33 initial missing-answer cases; streaming/nonstreaming checks passed. Thinking remains enabled. For evaluation, final content is taken after `</think>` when present; normally stopped direct content is retained, while unclosed length-limited output is not scored as an answer. | |
| In llama.cpp content-only mode, remove an echoed opening `<think>` generation-prefix tag before applying answer extraction. It is protocol framing, not answer text. The GGUF preflight was restarted after verifying this correction and the template override; it is excluded from the reported scores. | |
| The author's private VeriLoop Harness is not included or reproduced. Their SWE/Terminal/DeepSWE scores are **not scores for these weights**. This evaluation does not establish long-context, tool-use, vision, agentic performance, or production suitability. Reduced refusal can affect inappropriate-content handling; applications need their own behavior controls. Full semantic refusal review and comprehensive capability equivalence have not been established. | |
| Apache-2.0 is retained from the fixed upstream source; see LICENSE. Source training belongs to its original authors. Community changes are quantization. See release.json and SHA256SUMS for provenance and integrity. | |
| ## Complete measurement records and historical comparison | |
| See [all six result columns, test standards and limitations](HISTORICAL-COMPARISON.md), [all 9,084 per-item metric records](evaluation-items.json), [question IDs and hashes](evaluation-question-indices.json), and [400 refusal-probe metric records](evaluation-refusal-items.json). No full-precision baseline was measured. Historical results are not a strictly identical-runtime comparison. | |