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 CONTAMINATION.md from org2ai/Wald-4B: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
-
https://huggingface.co/org2ai/Wald-4B/resolve/8bb838defd691d09bf7f6bb67499152284b9651f/CONTAMINATION.md
- Command line
-
hf download hf://org2ai/Wald-4B@8bb838defd691d09bf7f6bb67499152284b9651f/CONTAMINATION.md
-
curl -L -o CONTAMINATION.md https://huggingface.co/org2ai/Wald-4B/resolve/8bb838defd691d09bf7f6bb67499152284b9651f/CONTAMINATION.md
2.05 kB
Evaluation notes
- Known strict training overlaps were filtered and training-file hashes checked. Weak/semantic overlap and foundation-model pretraining contamination are not ruled out; HLE training overlap has not been independently cleared. Benchmark samples informed development, so this is not a sealed evaluation.
- The complete 54.59 run used a frozen 256-row XL-dev calibration fit after excluding known DI matches. No DI labels or DI index score were used to fit it. HLE calibration screening found no additional match.
- The earlier 54.02 sample used the previous calibration table with known overlaps and remains historical. Old v1.0 disclosures apply to its own weights and are preserved under
v1.0-legacy. - Full-run response artifacts are unchanged. No request payloads or generated reasoning text are included. Official maintainer serial latency validation and leaderboard admission remain pending.
v1.2 (checkpoint 02600-f19)
- v1.2 was trained only on perturbed and unperturbed copies of v1.1's own training questions. A Decision Index blocklist scan of its training and hold-out rows found 0 strict matches (329 weak alerts, all states shorter than 8 words).
- JevAdvBench is evaluation-only. An 8-gram overlap check of every inserted text against every JevAdvBench string (the clean questions and all 9,744 attacked variants) found 0 hits. The perturbation kinds were chosen after v1.1's per-attack JevAdvBench results were known, so the benchmark's attack families informed the training design; its texts did not. Robustness to other attack types is not measured.
- The JevBench public items were again a development scoreboard: v1.2 had a pass line of at least 201/231 set before training. They were never training data.
- The 54.59 complete-suite Decision Index result and the files under
evaluation/other thanevaluation/v1.2/belong to v1.1. v1.2 has only a one-pass read of a 6,948-request sample. - v1.2 uses v1.1's temperature table unchanged. No benchmark labels were used to fit it.