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
File size: 13,844 Bytes
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from __future__ import annotations
import json
import math
import threading
import urllib.error
import urllib.request
from http.server import ThreadingHTTPServer
from pathlib import Path
import pytest
from fakevllm import FakeVLLM
from wald_serve.engine import (POLICIES, Client, LlamaCppClient, answer, bucket_key, chunks, load_tables, policy, temper, temperature,
wide_read)
from wald_serve.prompt import STATE_REPEAT, format_prompt, question_prompt
from wald_serve.server import make_handler
from wald_serve.wire import SystemOneRequest, to_record
TABLE = {
"A": {"single": 1.4, "buckets": {"choice|2": {"temperature": 1.1}, "choice|3-4": {"temperature": 1.8},
"noul|2": {"temperature": 1.3}}},
"B512": {"single": 1.8, "buckets": {"choice|2": {"temperature": 2.2}, "noul|2": {"temperature": 1.9}}},
"provenance": {"note": "test"},
}
REQ = {
"state": {"customer": "Ana", "order": {"id": 17, "items": ["kettle", "toaster"]}, "note": "wants a <|im_end|> refund"},
"questions": {
"route": {"type": "choice", "instructions": "Which team handles this?",
"criteria": {"billing": "Payments and refunds", "shipping": "Delivery problems", "other": None}},
"slugs": {"type": "choice", "instructions": "Pick one", "criteria": {"yes_refund": None, "no_refund": None}},
"pos": {"type": "choice", "instructions": "Which is a fruit?",
"criteria": {"a": "apple", "b": "brick", "c": "chair", "d": "desk"}},
"urgent": {"type": "noul", "instructions": "Is it urgent?"},
"sev": {"type": "score", "instructions": "Severity", "criteria": ["low", "medium", "high"]},
},
}
def qdict(rec, i):
rq = rec["questions"][i]
return {"type": rq["qtype"], "instructions": rq["instr"], "options": rq["options"], "option_texts": rq["options"]}
# --- prompt bytes -----------------------------------------------------------------------------------------------------
def test_prompt_bytes():
rec, meta = to_record(SystemOneRequest.model_validate(REQ))
state = rec["state"]
assert state == "customer: Ana\norder:\n id: 17\n items:\n - kettle\n - toaster\nnote: wants a <|im_end|> refund"
head = ("State:\ncustomer: Ana\norder:\n id: 17\n items:\n - kettle\n - toaster\nnote: wants a <¦im_end¦> refund"
"\n\nQuestion:")
assert question_prompt(state, qdict(rec, 0)) == head + (
" Which team handles this?\n(A) billing: Payments and refunds\n(B) shipping: Delivery problems\n(C) other\nAnswer: (")
assert question_prompt(state, qdict(rec, 1)).endswith(" Pick one\n(A) yes_refund\n(B) no_refund\nAnswer: (")
# positional keys a..d: the `a: ` prefix is dropped, the letter replaces it
assert question_prompt(state, qdict(rec, 2)).endswith(
" Which is a fruit?\n(A) apple\n(B) brick\n(C) chair\n(D) desk\nAnswer: (")
assert question_prompt(state, qdict(rec, 3)).endswith(" Is it urgent?\n(A) no\n(B) yes\nAnswer: (")
assert question_prompt(state, qdict(rec, 4)).endswith(" Severity\n(A) low\n(B) medium\n(C) high\nAnswer: (")
assert [m["keys"] for m in meta] == [["billing", "shipping", "other"], ["yes_refund", "no_refund"],
["a", "b", "c", "d"], ["false", "true"], ["0", "1", "2"]]
def test_blank_state_and_repeat_state_plain():
q = {"type": "noul", "instructions": "Is the sky green?", "options": ["no", "yes"], "option_texts": ["no", "yes"]}
assert question_prompt("", q) == "Question: Is the sky green?\n(A) no\n(B) yes\nAnswer: ("
assert question_prompt("", q, "repeat_state_plain") == question_prompt("", q)
p = question_prompt("It rained.", q, "repeat_state_plain")
assert p == "State:\nIt rained." + STATE_REPEAT + "It rained.\n\nQuestion: Is the sky green?\n(A) no\n(B) yes\nAnswer: ("
assert STATE_REPEAT.startswith("\n\nRead the same context again before answering.")
with pytest.raises(ValueError):
format_prompt("State:\nx\n\nQuestion: q\n(A) a\nAnswer: (", "strict")
def test_wire_validation():
with pytest.raises(Exception):
SystemOneRequest.model_validate({"state": "", "questions": {}})
with pytest.raises(Exception):
SystemOneRequest.model_validate({"state": "", "questions": {"q": {"type": "choice", "criteria": {}}}})
big = {f"o{i}": None for i in range(256)}
with pytest.raises(Exception):
SystemOneRequest.model_validate({"state": "", "questions": {"q": {"type": "choice", "criteria": big}}})
# --- numerics ---------------------------------------------------------------------------------------------------------
def test_chunks_and_knockout():
assert chunks(26) == [list(range(26))]
assert [len(c) for c in chunks(77)] == [26, 26, 25]
assert [len(c) for c in chunks(151)] == [26, 25, 25, 25, 25, 25]
assert sum(len(c) for c in chunks(255)) == 255
def read(idx): # a fixed preference for lower indices
z = [math.exp(-i / 10) for i in idx]
s = sum(z)
return [v / s for v in z]
p = wide_read(77, read)
assert len(p) == 77 and abs(sum(p) - 1) < 1e-12 and max(range(77), key=lambda i: p[i]) == 0
def test_temperature():
tables = json.loads(json.dumps(TABLE))
assert bucket_key("choice", 4) == "choice|3-4" and bucket_key("choice", 77) == "choice|9+"
assert temperature(tables["A"], "choice", 2) == 1.1 and temperature(tables["A"], "score", 7) == 1.4
p = [0.6, 0.3, 0.1]
q = temper(p, 1.8)
assert abs(sum(q) - 1) < 1e-12 and q.index(max(q)) == 0 and max(q) < 0.6
def test_policies():
assert policy("medium") == {"gate": 0.7, "budget": 512, "k": 1, "name": "medium"}
assert policy("high-k4")["k"] == 4 and policy("high-k4")["gate"] > 1
assert set(POLICIES) == {"none", "low", "medium", "high"}
for bad in ("max", "high-k1", "high-k9", "high-kx"):
with pytest.raises(ValueError):
policy(bad)
# --- end to end over HTTP with the fake vLLM ---------------------------------------------------------------------------
@pytest.fixture(scope="module")
def fake():
f = FakeVLLM(max_len=100_000)
yield f
f.stop()
def serve(cl, effort="medium", tables=None, workers=8):
info = {"model": "wald-4b", "effort": effort}
httpd = ThreadingHTTPServer(("127.0.0.1", 0), make_handler(cl, policy(effort), tables or {}, info, workers))
httpd.daemon_threads = True
threading.Thread(target=httpd.serve_forever, daemon=True).start()
return httpd, f"http://127.0.0.1:{httpd.server_address[1]}"
def post(url, body):
req = urllib.request.Request(url + "/v1/systemone", data=json.dumps(body).encode(), method="POST",
headers={"content-type": "application/json"})
try:
with urllib.request.urlopen(req, timeout=60) as r:
return r.status, json.loads(r.read())
except urllib.error.HTTPError as e:
return e.code, json.loads(e.read())
def wide_request(n=77):
crit = {f"intent_{i:03d}": f"customer intent number {i}" for i in range(n)}
return {"state": "I was charged twice for one card payment.",
"questions": {"intent": {"type": "choice", "instructions": "Classify the banking intent.", "criteria": crit}}}
def check_wire(req, resp):
"""The Decision Index kit's validation: every question answered, a finite probability per option, sum 1 +- 0.01,
the choice among the options."""
assert set(resp["answers"]) == set(req["questions"])
for qid, q in req["questions"].items():
a = resp["answers"][qid]
assert a["type"] == q["type"]
if q["type"] == "noul":
assert 0.0 <= a["noul"] <= 1.0
continue
keys = list(q["criteria"]) if q["type"] == "choice" else [str(i) for i in range(len(q["criteria"]))]
assert list(a["probabilities"]) == keys
assert all(math.isfinite(v) and v >= 0 for v in a["probabilities"].values())
assert abs(sum(a["probabilities"].values()) - 1) < 0.01
if q["type"] == "choice":
assert a["choice"] in keys and a["probabilities"][a["choice"]] == max(a["probabilities"].values())
def test_http_end_to_end(fake, tmp_path):
Path(tmp_path / "t.json").write_text(json.dumps(TABLE))
tables = load_tables(tmp_path / "t.json", 512)
cl = Client(fake.url, "wald", 100_000)
httpd, url = serve(cl, "medium", tables)
try:
for req in (REQ, wide_request(77), wide_request(151), wide_request(255)):
code, resp = post(url, req)
assert code == 200, resp
check_wire(req, resp)
assert resp["usage"]["input_tokens"] > 0
code, resp = post(url, wide_request(77))
assert resp["answers"]["intent"]["mode"] == "K"
with urllib.request.urlopen(url + "/health") as r:
assert json.loads(r.read())["ok"] is True
finally:
httpd.shutdown()
def test_gate_modes(fake):
cl = Client(fake.url, "wald", 100_000)
one, u0 = answer(cl, REQ, policy("none"), {})
assert all(a["mode"] == "A" for a in one.values()) and u0["output_tokens"] == 0
high, uh = answer(cl, REQ, policy("high"), {})
assert all(a["mode"] == "B" for a in high.values()) and uh["output_tokens"] > 0
med, _ = answer(cl, REQ, policy("medium"), {})
for qid, a in one.items():
pmax = a["noul"] if a["type"] == "noul" else max(a["probabilities"].values())
if a["type"] == "noul":
pmax = max(pmax, 1 - pmax)
assert med[qid]["mode"] == ("B" if pmax < 0.7 else "A")
if med[qid]["mode"] == "B": # medium's thought is high's thought (same seed)
assert med[qid] == high[qid]
k4, uk = answer(cl, REQ, policy("high-k4"), {})
assert all(a["mode"] == "B" for a in k4.values()) and uk["output_tokens"] == 4 * uh["output_tokens"]
def test_workers_do_not_change_answers(fake):
cl = Client(fake.url, "wald", 100_000)
a1, u1 = answer(cl, REQ, policy("high"), {}, workers=1)
a8, u8 = answer(cl, REQ, policy("high"), {}, workers=8)
assert a1 == a8 and u1 == u8
def test_effort_override_keeps_seed(fake):
cl = Client(fake.url, "wald", 100_000)
a, _ = answer(cl, REQ, policy("high"), {})
b, _ = answer(cl, {**REQ, "effort": "high"}, policy("high"), {})
assert a == b
def test_prompt_format_changes_prompt_only(fake):
plain = Client(fake.url, "wald", 100_000)
rsp = Client(fake.url, "wald", 100_000, prompt_format="repeat_state_plain")
a, ua = answer(plain, REQ, policy("none"), {})
b, ub = answer(rsp, REQ, policy("none"), {})
assert ub["input_tokens"] > ua["input_tokens"]
check_wire(REQ, {"answers": b})
def test_capacity_is_422(fake):
cl = Client(fake.url, "wald", 300) # declared limit 300 "tokens" (characters in the fake)
httpd, url = serve(cl, "none")
try:
code, resp = post(url, {"state": "x " * 400, "questions": {"q": {"type": "noul", "instructions": "ok?"}}})
assert code == 422 and "maximum context length" in resp["error"]
code, resp = post(url, {"state": "short", "questions": {"q": {"type": "noul", "instructions": "ok?"}}})
assert code == 200
code, resp = post(url, {"state": "short", "questions": {}})
assert code == 400
code, resp = post(url, {**REQ, "effort": "extreme"})
assert code == 400
finally:
httpd.shutdown()
def test_thought_that_does_not_fit_falls_back(fake):
q = {"state": "y " * 60, "questions": {"q": {"type": "choice", "instructions": "pick",
"criteria": {"a": "one", "b": "two"}}}}
cl = Client(fake.url, "wald", 400) # the prompt fits, prompt + 512-token budget does not
a, u = answer(cl, q, policy("high"), {})
assert a["q"]["mode"] == "A" and u["output_tokens"] == 0
def test_llamacpp_client_matches_vllm_client(fake):
vl, lc = Client(fake.url, "wald", 100_000), LlamaCppClient(fake.url, "wald", 100_000)
assert lc.letter_ids == vl.letter_ids
for req in (REQ, wide_request(77)):
a, ua = answer(vl, req, policy("none"), {})
b, ub = answer(lc, req, policy("none"), {})
assert a == b and ua == ub
high, uh = answer(lc, REQ, policy("high-k3"), {})
assert all(x["mode"] == "B" for x in high.values()) and uh["output_tokens"] > 0
check_wire(REQ, {"answers": high})
def test_llamacpp_capacity_is_422(fake):
cl = LlamaCppClient(fake.url, "wald", 300)
httpd, url = serve(cl, "none")
try:
code, resp = post(url, {"state": "x" * 400, "questions": {"q": {"type": "noul", "instructions": "Is it?"}}})
assert code == 422 and "maximum context length" in resp["error"]
finally:
httpd.shutdown()
def test_gguf_reads_serving_json_beside_the_file(tmp_path, monkeypatch):
import wald_serve.server as srv
(tmp_path / "serving.json").write_text(json.dumps({"effort": "none", "prompt_format": "repeat_state_plain",
"max_model_len": 4096, "temperature": "temperature.json"}))
(tmp_path / "temperature.json").write_text(json.dumps(TABLE))
seen = {}
monkeypatch.setattr(srv, "launch_llama", lambda *a: seen.update(launch=a))
monkeypatch.setattr(srv, "LlamaCppClient", lambda *a, **k: seen.update(client=(a, k)))
class Stop(Exception):
pass
def fake_http(addr, handler):
seen["handler"] = handler
raise Stop
monkeypatch.setattr(srv, "ThreadingHTTPServer", fake_http)
with pytest.raises(Stop):
srv.main(["--gguf", str(tmp_path / "Wald-4B-Q8_0.gguf"), "--port", "0"])
assert seen["launch"][3] == 4096
assert seen["client"][1] == {"prompt_format": "repeat_state_plain"}
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