Text Generation
Transformers
Safetensors
GGUF
English
qwen3_5
image-text-to-text
decision-model
typed-decisions
calibration
calibrated-probabilities
classification
tool-selection
agent-routing
decision-index
jevbench
jev-compatible
systemone
wald
wald-q4b
qwen3.5
4b
vllm
reasoning
llama.cpp
conversational
Eval Results (legacy)
Instructions to use org2ai/Wald-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use org2ai/Wald-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="org2ai/Wald-4B") 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("org2ai/Wald-4B") model = AutoModelForMultimodalLM.from_pretrained("org2ai/Wald-4B", 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 org2ai/Wald-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "org2ai/Wald-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "org2ai/Wald-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/org2ai/Wald-4B
- SGLang
How to use org2ai/Wald-4B 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 "org2ai/Wald-4B" \ --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": "org2ai/Wald-4B", "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 "org2ai/Wald-4B" \ --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": "org2ai/Wald-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use org2ai/Wald-4B with Docker Model Runner:
docker model run hf.co/org2ai/Wald-4B
main = Wald-Q4B v1.2 (02600-f19, robustness release): weights from v1.2-release, v1.2 serving.json (effort none) and runbook; card: v1.2 on main, v1.1 at tag v1.1
981b91b verified Download evaluation/v1.2/summary.json from org2ai/Wald-4B: direct link, hf CLI and curl.
- Browser
- Download file 6.99 kB
-
https://huggingface.co/org2ai/Wald-4B/resolve/8bb838defd691d09bf7f6bb67499152284b9651f/evaluation/v1.2/summary.json
- Command line
-
hf download hf://org2ai/Wald-4B@8bb838defd691d09bf7f6bb67499152284b9651f/evaluation/v1.2/summary.json
-
curl -L -o summary.json https://huggingface.co/org2ai/Wald-4B/resolve/8bb838defd691d09bf7f6bb67499152284b9651f/evaluation/v1.2/summary.json
6.99 kB
| { | |
| "schema": "wald-q4b-release-evaluation/1", | |
| "release": "Wald-Q4B v1.2", | |
| "checkpoint": "02600-f19", | |
| "parent": "Wald-Q4B v1.1 (022D0-f7, revision v1.1)", | |
| "date": "2026-10-01", | |
| "status": "All numbers are self-run and self-reported. Nothing here is a leaderboard result and nothing was submitted for v1.2.", | |
| "configuration": "packaged wald-serve 0.1.0 + vLLM 0.30.0, BF16, effort none (one pass), prompt format repeat_state_plain, v1.1 temperature table; one NVIDIA RTX 5090 32 GB unless noted", | |
| "weights_sha256": { | |
| "model-00001-of-00002.safetensors": "0b15067f769e7388bd598aabafa2ea3211a3a0c4e49e47d07c4f65c65ffc25dd", | |
| "model-00002-of-00002.safetensors": "7cff102b314edeb3dc5abfa7063f72ad3e888884da237601239dc3582074120a" | |
| }, | |
| "jevadvbench": { | |
| "benchmark": { | |
| "repo": "JevAdvBench/JevAdvBench@3218e05", | |
| "paper": "arXiv 2609.31142", | |
| "questions": 812, | |
| "scenarios": 66, | |
| "attack_variants": 9744, | |
| "data_licence": "CC BY-NC 4.0 (used for evaluation only; no items are redistributed here)" | |
| }, | |
| "metric": "flip = the decision on the attacked question differs from the same model's decision on the clean question; % of 812 questions; 95% scenario-cluster bootstrap intervals (66 clusters, 2,000 resamples), the authors' analysis code", | |
| "jev_column": "jev-1.13.0 responses as released with the benchmark, scored by the same code", | |
| "flip_rate_pct": { | |
| "Q1": { | |
| "name": "word edits", | |
| "jev_1_13": 1.0, | |
| "v1_1": 1.6, | |
| "v1_2": 1.6, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": 0.0, | |
| "ci95": [ | |
| -0.8, | |
| 0.8 | |
| ] | |
| } | |
| }, | |
| "Q2": { | |
| "name": "paraphrase", | |
| "jev_1_13": 1.5, | |
| "v1_1": 1.6, | |
| "v1_2": 1.4, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -0.2, | |
| "ci95": [ | |
| -1.1, | |
| 0.5 | |
| ] | |
| } | |
| }, | |
| "Q3": { | |
| "name": "unrelated sentences in the question", | |
| "jev_1_13": 4.6, | |
| "v1_1": 14.9, | |
| "v1_2": 4.6, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -10.3, | |
| "ci95": [ | |
| -13.5, | |
| -7.2 | |
| ] | |
| } | |
| }, | |
| "T1": { | |
| "name": "unrelated note in the state", | |
| "jev_1_13": 2.2, | |
| "v1_1": 2.8, | |
| "v1_2": 1.7, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -1.1, | |
| "ci95": [ | |
| -2.4, | |
| 0.0 | |
| ] | |
| } | |
| }, | |
| "T2": { | |
| "name": "observer's opinion in the state", | |
| "jev_1_13": 12.1, | |
| "v1_1": 16.9, | |
| "v1_2": 5.5, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -11.3, | |
| "ci95": [ | |
| -14.6, | |
| -8.2 | |
| ] | |
| } | |
| }, | |
| "T3": { | |
| "name": "opinion through an analogy", | |
| "jev_1_13": 6.9, | |
| "v1_1": 12.7, | |
| "v1_2": 6.3, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -6.4, | |
| "ci95": [ | |
| -8.8, | |
| -3.6 | |
| ] | |
| } | |
| }, | |
| "P1": { | |
| "name": "direct override", | |
| "jev_1_13": 8.9, | |
| "v1_1": 7.9, | |
| "v1_2": 5.7, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -2.2, | |
| "ci95": [ | |
| -3.6, | |
| -0.9 | |
| ] | |
| } | |
| }, | |
| "P2": { | |
| "name": "authority impersonation", | |
| "jev_1_13": 10.1, | |
| "v1_1": 13.7, | |
| "v1_2": 6.7, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -7.0, | |
| "ci95": [ | |
| -9.0, | |
| -5.1 | |
| ] | |
| } | |
| }, | |
| "P3": { | |
| "name": "fake validation note", | |
| "jev_1_13": 8.1, | |
| "v1_1": 10.3, | |
| "v1_2": 7.8, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -2.6, | |
| "ci95": [ | |
| -4.2, | |
| -1.0 | |
| ] | |
| } | |
| } | |
| }, | |
| "mean_of_nine_delivered_attacks_pct": { | |
| "jev_1_13": 6.1, | |
| "v1_1": 9.2, | |
| "v1_2": 4.6, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -4.6, | |
| "ci95": [ | |
| -5.5, | |
| -3.6 | |
| ] | |
| }, | |
| "v1_2_minus_jev_pp": { | |
| "point": -1.6, | |
| "ci95": [ | |
| -2.7, | |
| -0.4 | |
| ] | |
| } | |
| }, | |
| "questions_flipped_by_at_least_one_attack_pct": { | |
| "jev_1_13": 29.2, | |
| "v1_1": 41.7, | |
| "v1_2": 20.7 | |
| }, | |
| "clean_accuracy_143_human_reviewed_pct": { | |
| "jev_1_13": 87.4, | |
| "v1_1": 79.0, | |
| "v1_2": 76.2, | |
| "v1_2_minus_v1_1_paired_pp": { | |
| "point": -2.8, | |
| "ci95": [ | |
| -6.2, | |
| -0.6 | |
| ] | |
| } | |
| }, | |
| "clean_decisions_equal_to_v1_1_pct": 98.6, | |
| "note": "The perturbation kinds used to train v1.2 were chosen after v1.1's per-attack results were known. No JevAdvBench text was used in training (8-gram overlap 0)." | |
| }, | |
| "jevbench_public_231": { | |
| "harness": "fstandhartinger/jevbench@9ec6f15a, jevbench.cli run + summarize, typesafe adapter, serial, loopback", | |
| "dataset_hash": "dc3995d8ae1e2fc8e81ce38431add509eb8bb39b85aadfd0c7c32079382dde51", | |
| "v1_2": { | |
| "none": { | |
| "correct": 204, | |
| "of": 231, | |
| "easy": "48/48", | |
| "original": "72/72", | |
| "hard": "84/111", | |
| "ece": 0.045, | |
| "brier": 0.191, | |
| "gpu": "RTX 5090" | |
| }, | |
| "medium": { | |
| "correct": 198, | |
| "of": 231, | |
| "ece": 0.037, | |
| "brier": 0.208, | |
| "gpu": "RTX 5090", | |
| "runs": 1 | |
| }, | |
| "high": { | |
| "correct": 198, | |
| "of": 231, | |
| "ece": 0.072, | |
| "brier": 0.204, | |
| "gpu": "RTX 5090", | |
| "runs": 1 | |
| } | |
| }, | |
| "v1_1": { | |
| "none": { | |
| "correct": 203, | |
| "of": 231, | |
| "ece": 0.041, | |
| "brier": 0.188, | |
| "gpu": "RTX PRO 6000" | |
| }, | |
| "medium": { | |
| "correct": 205, | |
| "of": 231, | |
| "gpu": "RTX PRO 6000", | |
| "runs": 1 | |
| } | |
| }, | |
| "note": "The public items were a development scoreboard (never training data); not a held-out result. Latency was not measured for v1.2." | |
| }, | |
| "decision_index_0_2_1_sample": { | |
| "requests": 6948, | |
| "read": "one pass (no thinking), both versions on the same RTX 5090", | |
| "metric": "balanced_skill, bootstrap B=300", | |
| "v1_1": { | |
| "value": 49.76, | |
| "ci95": [ | |
| 47.82, | |
| 51.35 | |
| ] | |
| }, | |
| "v1_2": { | |
| "value": 50.03, | |
| "ci95": [ | |
| 48.18, | |
| 51.65 | |
| ] | |
| }, | |
| "v1_2_minus_v1_1_paired": { | |
| "point": 0.27, | |
| "ci95": [ | |
| -0.36, | |
| 1.05 | |
| ] | |
| }, | |
| "note": "A sample, not the complete suite. Not comparable with the 54.59 complete-suite result, which was measured on v1.1 with effort high. v1.2 has no complete-suite run." | |
| }, | |
| "internal_non_regression_sets": { | |
| "note": "Our own sets, not public benchmarks; one pass.", | |
| "xl_int": { | |
| "v1_1": 56.0, | |
| "v1_2": 56.4 | |
| }, | |
| "tool_selection_2857_items_accuracy_pct": { | |
| "v1_1": 87.15, | |
| "v1_2": 87.22 | |
| } | |
| } | |
| } | |