Instructions to use Meanblock/JEV-CPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meanblock/JEV-CPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Meanblock/JEV-CPU")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 31,568 Bytes
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<title>JEV-CPU — Running Semantic-If Decisions on a CPU (Technical Report)</title>
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<header class="title">
<div class="kicker">Technical Report · Independent Project</div>
<h1>JEV-CPU: Running Semantic-If Decisions on a CPU</h1>
<div class="authors">leesk212 · <span class="muted">Meanblock</span></div>
<div class="authors muted">September 19, 2026 · v1</div>
<div class="badges">
<a href="https://github.com/leesk212/JEV-CPU">GitHub</a>
<a href="https://huggingface.co/Meanblock/JEV-CPU">Hugging Face</a>
<a href="https://github.com/TheoLeeCJ/SemIf">Upstream: SemIf</a>
</div>
</header>
<div class="abstract">
<p><strong>Abstract.</strong> <em>Semantic-if</em> decisions — <em>route this</em>, <em>is this a policy
violation</em>, <em>what severity is this incident</em> — are usually answered by having a chat model
generate text that software then parses back into a branch. SemIf (an open reproduction of TypeSafe's
Jev pattern) instead reads a decision directly from a model's <em>option logits</em> in a single
forward pass, with no text generated. SemIf targets a GPU holding a 4B model. This report describes
<strong>JEV-CPU</strong>, a small adaptation that runs the <em>same</em> engine on a commodity CPU with
a 0.6B open model, and reports measurements from an 8 GB, GPU-less machine: qualitative decisions
across eight domains, a latency-versus-input-length curve, and the resulting practical input ceiling.
We make no claim of methodological novelty — the contribution is engineering and empirical: showing the
method is device-agnostic, quantifying where CPU latency (not memory or the model) becomes the limit,
and documenting how accuracy scales with model size. All code, weights pointer, and demos are public.</p>
</div>
<h2 id="toc">Contents</h2>
<nav class="toc">
<a href="#intro">1. Introduction</a>
<a href="#background">2. Background</a>
<a href="#method">3. Method: reading a decision from logits</a>
<a href="#system">4. System: the CPU port</a>
<a href="#domains">5. Qualitative results across domains</a>
<a href="#latency">6. Latency and input-length limits</a>
<a href="#scaling">7. Accuracy scaling with model size</a>
<a href="#outlook">7.1 Outlook: GPU serving & a production JEV</a>
<a href="#limitations">8. Limitations</a>
<a href="#conclusion">9. Conclusion</a>
<a href="#repro">10. Reproducibility</a>
<a href="#appendix">Appendix A. Code-level walkthrough</a>
<a href="#refs">References</a>
</nav>
<figure>
<img src="assets/jev-cpu-demo.gif" alt="JEV-CPU deciding live across eight domains on CPU">
<figcaption>Figure 1. JEV-CPU running live on a CPU across eight domains: the state is typed in, criteria
are added, and each decision is read from <code>Qwen3-0.6B</code>'s option logits in ~1 s — no text generated.</figcaption>
</figure>
<h2 id="intro">1. Introduction</h2>
<p>Most decisions inside software agents are small and typed: choose a queue, pick a severity, decide
whether evidence supports a claim. A chat model can answer them, but generating an answer sentence and
parsing it back into an <code>if</code> is slow and brittle. A <em>decision-native</em> alternative is to
present the options as tokens and read the model's probability over exactly those tokens in one forward
pass. TypeSafe's closed <em>Jev</em> service popularized this interface; <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>
reproduces the interface pattern with open models on a GPU.</p>
<p>This report asks a narrow, practical question: <strong>does the method still work with no GPU and a
tiny model, and where does it break down?</strong> We port SemIf's scoring to CPU (<a href="#system">§4</a>),
run it across eight domains (<a href="#domains">§5</a>), and measure the latency wall that determines the
usable input size (<a href="#latency">§6</a>).</p>
<h2 id="background">2. Background</h2>
<p>Reading a categorical decision from an LM's next-token distribution — rather than sampling text — is a
well-established idea (verbalizer-style zero-shot classification, answer-token scoring, NLI cross-encoders).
We claim <strong>no novelty</strong> for the mechanism. JEV-CPU is a CPU adaptation and empirical study of
SemIf, which is itself an independent reproduction of the Jev pattern; Jev and TypeSafe are the property of
their owners and are not affiliated with this work.</p>
<h2 id="method">3. Method: reading a decision from logits</h2>
<p>A decision is one record: a <code>state</code> (evidence), a <code>question</code> (criterion), and
2–16 typed <code>options</code>. The <code>direct</code> scoring path is unchanged from SemIf:</p>
<ol>
<li><strong>Letter-choice prompt.</strong> Each option is labeled <code>A</code>, <code>B</code>,
<code>C</code>… in a chat turn whose system instruction asks for a single uppercase letter and nothing
else. With the generation prompt applied, the next token the model would emit is the answer letter.</li>
<li><strong>Single-token pinning.</strong> Each letter is verified to encode to exactly one token that
round-trips and does not perturb the prompt's tokenization, so every option maps to one clean, comparable
vocabulary slot.</li>
<li><strong>One forward pass.</strong> The prompt is run through the model once; we keep the logits at the
final position — the distribution over the next token. No sampling, no decode loop, no JSON to repair.</li>
<li><strong>Softmax over slots.</strong> From that full-vocabulary logit vector we gather only the option
letters' token ids and softmax over those, giving a probability per option conditional on the declared set.</li>
</ol>
<pre><code>logits = model(**inputs, use_cache=False).logits[:, -1, :] # (vocab,)
selected = logits[slot_ids] # logits at tokens A, B, C, ...
probs = softmax(selected) # distribution over the declared options
winner = options[argmax(probs)]</code></pre>
<p>Because it is one forward pass reading fixed positions, latency is dominated by prompt <em>prefill</em>,
not by generation length — the property JEV-CPU relies on to stay usable on a CPU.</p>
<h2 id="system">4. System: the CPU port</h2>
<p>SemIf forces a GPU in exactly one place — its model loader checks for a single CUDA device and loads with
<code>device_map={"":"cuda:0"}</code>, <code>bfloat16</code>. Everything downstream is device-agnostic: the
scoring code follows <code>device = next(model.parameters()).device</code> and the only CUDA-specific call,
<code>torch.cuda.synchronize()</code>, is guarded by <code>if device.type == "cuda"</code> (a no-op on CPU).</p>
<p>JEV-CPU therefore changes <strong>only the loader</strong>: a CPU / <code>float32</code> loader that reuses
SemIf's original, unmodified scoring functions. A small standard-library web server exposes the engine with a
three-pane UI (state, criteria, results). Swapping the model is a one-line change (<code>MODEL = …</code>);
the engine and UI are model-agnostic.</p>
<figure>
<img src="assets/jev-cpu-ui.png" alt="JEV-CPU three-pane web UI">
<figcaption>Figure 2. The web UI: state (top-left), criteria (bottom-left), and per-option probability
bars with the chosen option (right). The footer names the method (JEV-CPU engine) and the brain model.</figcaption>
</figure>
<h2 id="domains">5. Qualitative results across domains</h2>
<p>Using <code>Qwen/Qwen3-0.6B</code> in <code>float32</code> on CPU, we ran the same engine across eight
domains (Figure 1). Each decision took ≈1 s.</p>
<div class="tablewrap">
<table>
<thead><tr><th>Domain</th><th>Criterion</th><th>Decision</th><th class="n">Forward</th></tr></thead>
<tbody>
<tr><td>Customer support</td><td>Sentiment</td><td><span class="ok">negative — 97.4%</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Customer support</td><td>Route to team</td><td><span class="ok">billing — 100%</span></td><td class="n">~1.0 s</td></tr>
<tr><td>Content moderation</td><td>Policy violation?</td><td><span class="ok">violation — 99.3%</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Content moderation</td><td>Recommended action</td><td><span class="ok">warn — 69.2%</span></td><td class="n">~1.0 s</td></tr>
<tr><td>Code-review triage</td><td>Merge risk</td><td><span class="ok">high — 99.8%</span></td><td class="n">~1.2 s</td></tr>
<tr><td>Code-review triage</td><td>PR disposition</td><td><span class="ok">block — 94.9%</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Incident / DevOps</td><td>Severity</td><td><span class="ok">sev1 — 100%</span></td><td class="n">~1.2 s</td></tr>
<tr><td>Incident / DevOps</td><td>Page on-call now?</td><td><span class="ok">page_now — 100%</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Email intent</td><td>Primary intent</td><td><span class="ok">sales — 100%</span></td><td class="n">~1.2 s</td></tr>
<tr><td>Compliance gate</td><td>Change ticket required?</td><td><span class="ok">required — 100%</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Loan / credit risk</td><td>Credit risk</td><td><span class="ok">high — 100%</span></td><td class="n">~1.2 s</td></tr>
<tr><td>Loan / credit risk</td><td>Recommended decision</td><td><span class="bad">approve — 82.8% ⚠︎</span></td><td class="n">~1.1 s</td></tr>
<tr><td>Support prioritization</td><td>Priority</td><td><span class="ok">p1 — 100%</span></td><td class="n">~1.2 s</td></tr>
</tbody>
</table>
</div>
<p class="muted">These are single illustrative runs, not a benchmark; they show the interface and the
qualitative behavior. The loan <em>decision</em> row is a deliberate example of a small-model slip
(see <a href="#limitations">§8</a>).</p>
<h2 id="latency">6. Latency and input-length limits</h2>
<p>Two numbers are often conflated. The model's context window is <strong>40,960 tokens</strong> — large even
at 0.6B, since context length comes from positional encoding, independent of parameter count. SemIf's engine
applies a default cap of <strong>4,096 tokens per decision</strong> as a guard (over-long prompts raise instead
of being silently truncated); it is configurable. Neither is the real constraint on CPU.</p>
<p>We measured one decision with a growing state on the 8 GB CPU box (peak RSS via <code>getrusage</code>):</p>
<div class="tablewrap">
<table>
<thead><tr><th class="n">Input tokens</th><th class="n">Forward (CPU)</th><th class="n">Peak RAM</th></tr></thead>
<tbody>
<tr><td class="n">254</td><td class="n">2.5 s</td><td class="n">~3.5 GB</td></tr>
<tr><td class="n">731</td><td class="n">5.6 s</td><td class="n">~3.5 GB</td></tr>
<tr><td class="n">1,363</td><td class="n">11.9 s</td><td class="n">~3.5 GB</td></tr>
<tr><td class="n">2,629</td><td class="n">26.0 s</td><td class="n">~3.5 GB</td></tr>
<tr><td class="n">5,165</td><td class="n">66.1 s</td><td class="n">~3.5 GB</td></tr>
<tr><td class="n">7,697</td><td class="n">117.4 s</td><td class="n">~3.5 GB</td></tr>
</tbody>
</table>
</div>
<p>RAM stayed flat at ~3.5 GB with no OOM even at 7,697 tokens — well past the 4,096 default — so the
ceiling on this box is <strong>prefill latency (roughly quadratic in length)</strong>, not memory or the model.</p>
<div class="warnbox">
<strong>Practical input ceiling.</strong> We treat <strong>≈7,700 tokens (~117 s)</strong> as the usable
maximum on this CPU: beyond it a single decision crosses <strong>~120 s</strong>, which is no longer useful,
so larger inputs are considered unsupported on this hardware. Interactive ~1 s decisions want short states
(≲~300 tokens). A GPU removes this wall — inputs up to the model's 40,960 become usable again.
</div>
<h2 id="scaling">7. Accuracy scaling with model size</h2>
<p><code>Qwen3-0.6B</code> is the smallest model on SemIf's ladder, chosen to fit in CPU RAM; it is the
accuracy floor, not the ceiling. From SemIf's own evaluation:</p>
<div class="tablewrap">
<table>
<thead><tr><th>Brain model</th><th class="n">Size</th><th class="n">Authored balanced acc.</th><th class="n">TypeSafe subset agr.</th></tr></thead>
<tbody>
<tr><td>Qwen3-0.6B <span class="muted">(JEV-CPU default)</span></td><td class="n">0.6 B</td><td class="n">0.440</td><td class="n">0.407</td></tr>
<tr><td>MiniCPM5-2B</td><td class="n">2 B</td><td class="n">0.686</td><td class="n">0.637</td></tr>
<tr><td>Qwen3.5-4B</td><td class="n">4 B</td><td class="n"><strong>0.813</strong></td><td class="n"><strong>0.845</strong></td></tr>
</tbody>
</table>
</div>
<p>Because the engine is model-agnostic, moving up the ladder is a one-line change — at the cost of RAM and
compute that exceed this CPU box (a 4B model wants a GPU, SemIf's target). The takeaway: <strong>JEV-CPU shows
the method runs anywhere; accuracy scales with the model you point it at.</strong></p>
<h3 id="outlook">7.1 Outlook — GPU serving and a production JEV</h3>
<p>The two axes of this report — accuracy (§7) and CPU latency (§6) — are usually assumed to trade off, but a
GPU relaxes <em>both at once</em>. On CPU the wall is prefill: a single 7,697-token decision took ~117 s
(§6). A GPU changes the regime on three fronts:</p>
<ul>
<li><strong>Latency collapses.</strong> Prefill is exactly the workload GPUs are built for. SemIf reports a
4B model on a single RTX 3090 returning <strong>21 binary decisions in a median 1.02 s</strong>,
versus 5.3 s to generate the equivalent JSON array — the same logit-readout path, just not latency-bound.</li>
<li><strong>Input size grows.</strong> With the latency wall gone, the practical input rises from our
~7,700-token / ~120 s CPU ceiling toward the model's full <strong>40,960-token</strong> context — long
documents, transcripts, or code diffs become decidable in one pass.</li>
<li><strong>Throughput multiplies.</strong> Because a decision is one forward pass reading fixed positions,
decisions <em>batch</em> trivially, and shared-state prefix reuse amortizes one long state across many
criteria. SemIf measures <strong>~20 decisions/s</strong> (777 decisions in 38.8 s) with parallel
suffixes on one 3090.</li>
</ul>
<p>Put together, a larger open-weight model (4B+) served on a GPU is <strong>simultaneously more accurate,
accepts far larger inputs, and answers many decisions per second</strong> — all with typed, auditable outputs
and no generation to parse. That combination is the shape of a <strong>production, potentially commercial,
JEV</strong>: semantic-if offered as a low-latency, high-throughput hosted service, with per-tenant models and
batched shared-state decisions. In that framing, <strong>JEV-CPU is the floor</strong> — proof the method is
portable to any machine — and a <strong>GPU-served larger model is the ceiling</strong> that turns the same
engine into a product.</p>
<div class="note">
<strong>Scope.</strong> We did not run GPU experiments in this report. The latency and throughput figures
above are SemIf's published single-GPU (RTX 3090, 4B) measurements together with projections from our own
CPU curve (§6) — offered as an implication, not as measured results of this work. Quantifying a GPU-served
JEV (tokens/s, decisions/s, cost per million decisions, batching and concurrency) is the natural next study.
</div>
<h2 id="limitations">8. Limitations</h2>
<ul>
<li><strong>No new method.</strong> The logit-readout mechanism is prior art; this is a port and measurement.</li>
<li><strong>Small-model errors.</strong> At 0.6B, secondary decisions can be inconsistent — e.g. the loan
case flags <em>high risk</em> correctly yet still leans <em>approve</em>. Larger models resolve this (§7).</li>
<li><strong>Not a benchmark.</strong> §5 shows single runs for illustration; we do not report accuracy on a
held-out labeled set here. SemIf's repository contains the quantitative evaluation we cite in §7.</li>
<li><strong>Single-machine measurements.</strong> Latencies (§6) are from one 8 GB CPU box and will vary
with hardware, threads, and BLAS backend.</li>
<li><strong>Probabilities are conditional</strong> on the declared options and are uncalibrated as confidence.</li>
</ul>
<h2 id="conclusion">9. Conclusion</h2>
<p>Decision-native, logit-readout inference is not tied to a GPU or a large model. With only a loader change,
SemIf's engine runs on a commodity CPU with a 0.6B model, decides across many domains at ~1 s each, and
stays memory-stable well past its default token cap. On this hardware the honest limit is latency: inputs above
~7,700 tokens cross the ~120 s mark and are impractical, and small-model accuracy — not context or memory —
is what improves by scaling the model up. JEV-CPU is offered as a reproducible, minimal demonstration of that
floor; the same engine, given a larger open-weight model on a GPU, points toward the ceiling (§7.1) — an
accurate, high-throughput, low-latency semantic-if service, i.e. a production JEV.</p>
<h2 id="repro">10. Reproducibility</h2>
<p>Code, the web UI, the exact scripts behind §5–§6, and all demo GIFs are public:</p>
<ul>
<li>GitHub — <a href="https://github.com/leesk212/JEV-CPU">github.com/leesk212/JEV-CPU</a></li>
<li>Hugging Face — <a href="https://huggingface.co/Meanblock/JEV-CPU">huggingface.co/Meanblock/JEV-CPU</a></li>
</ul>
<pre><code>python3 -m venv .venv && source .venv/bin/activate
pip install --index-url https://download.pytorch.org/whl/cpu torch
pip install transformers accelerate
python semif_cpu.py # CLI: typed option probabilities
python server.py # web UI on http://localhost:8080</code></pre>
<hr>
<h2 id="appendix">Appendix A. Code-level walkthrough: turning an open-weight model into a decision engine</h2>
<p>A "JEV" / decision engine is <strong>not a fine-tune and not new weights</strong> — it is a way of
<em>calling</em> an ordinary open-weight causal language model so that a typed decision falls out of a single
forward pass. This appendix walks the full pipeline function by function, as it runs in JEV-CPU. The scoring
code is <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>'s, reproduced here verbatim for the report;
JEV-CPU changes only the model <em>loader</em> (<a href="#a7">A.7</a>). All snippets are from
<code>src/semif_phase1/{core,direct}.py</code>.</p>
<h3 id="a1">A.1 — The record and its validation</h3>
<p>A decision is one plain record: a <code>state</code> (evidence, string / JSON object / array), a
<code>question</code> (criterion), and 2–16 <code>options</code>, each with a stable <code>id</code> and a
human <code>description</code>. Validation is strict so nothing is silently coerced:</p>
<pre><code>def validate_row(row):
required = {"id", "state", "question", "options"}
if not required <= row.keys(): raise ValueError(...)
# state must be a nonempty, finite, JSON-serializable string/object/array
json.dumps(state, ensure_ascii=False, allow_nan=False)
# 2..16 options, each {id: str, description: str}, ids unique
if not isinstance(options, list) or not 2 <= len(options) <= len(LETTERS): raise ValueError(...)</code></pre>
<h3 id="a2">A.2 — Building a letter-choice prompt</h3>
<p>Each option is assigned an uppercase letter and the record is serialized into a two-turn chat. The system
turn constrains the model to answer with a single letter and nothing else — this is what makes the <em>next</em>
token the entire decision:</p>
<pre><code>LETTERS = "ABCDEFGHIJKLMNOP"
DIRECT_SYSTEM = ("Apply the supplied criterion to the supplied evidence. "
"Choose exactly one listed option. Respond with only its "
"uppercase letter, with no explanation or reasoning.")
def direct_messages(row):
payload = {
"evidence": row["state"],
"criterion": row["question"],
"options": [{"letter": LETTERS[i], "description": o["description"]}
for i, o in enumerate(row["options"])],
}
return [{"role": "system", "content": DIRECT_SYSTEM},
{"role": "user", "content": json.dumps(payload, ensure_ascii=False)}]</code></pre>
<h3 id="a3">A.3 — Pinning each option to exactly one token</h3>
<p>The readout compares the model's probability of each answer letter, so every letter must map to a
<strong>single, clean vocabulary slot</strong>. Each letter is checked to encode to exactly one token that
round-trips, with no collisions between options:</p>
<pre><code>def _slot_ids(tokenizer, count):
result = []
for letter in LETTERS[:count]:
encoded = tokenizer.encode(letter, add_special_tokens=False)
if len(encoded) != 1 or tokenizer.decode(encoded) != letter:
raise ValueError(f"Answer slot {letter!r} is not one exact round-trip token")
result.append(encoded[0])
if len(result) != len(set(result)):
raise ValueError("Answer-slot tokens collide")
return result # e.g. token ids for "A", "B", "C"</code></pre>
<h3 id="a4">A.4 — Encoding and verifying the answer boundary</h3>
<p>The chat template is applied with the generation prompt on and thinking disabled, then two invariants are
asserted: the prompt fits the token budget (no silent truncation), and appending any answer letter extends the
tokenization by exactly that one slot token — i.e. the boundary between prompt and answer is stable:</p>
<pre><code>def encode_prompt(tokenizer, row, max_tokens):
prompt = tokenizer.apply_chat_template(
direct_messages(row), tokenize=False,
add_generation_prompt=True, enable_thinking=False)
ids = tokenizer.encode(prompt, add_special_tokens=False)
if not ids or len(ids) > max_tokens:
raise ValueError("input tokens exceed limit; no truncation allowed")
slots = _slot_ids(tokenizer, len(row["options"]))
for letter, token in zip(LETTERS, slots):
if tokenizer.encode(prompt + letter, add_special_tokens=False) != ids + [token]:
raise ValueError(f"Answer boundary changes tokenization for slot {letter}")
return ids, slots, digest(prompt) # digest = sha256 for auditability</code></pre>
<h3 id="a5">A.5 — One forward pass, last-position logits</h3>
<p>The whole model is run <strong>once</strong>. We keep only the final position's logits — the distribution
over the next token. <code>logits_to_keep=1</code> (when the model supports it) tells the model to compute
just that row, avoiding a full-sequence logit tensor. There is no sampling and no decode loop:</p>
<pre><code>def _forward(model, inputs):
params = inspect.signature(model.forward).parameters
kwargs = dict(inputs, use_cache=False, return_dict=True)
if "logits_to_keep" in params:
kwargs["logits_to_keep"] = 1
return model(**kwargs).logits[:, -1, :] # shape (batch, vocab)</code></pre>
<h3 id="a6">A.6 — Gather the slots, softmax, decide</h3>
<p>From the full-vocabulary logit vector we index the option-letter tokens and softmax over <em>only</em>
those, giving a probability per option conditional on the declared set. Note the two device lines: the code
reads whatever device the model is on and only synchronizes under CUDA — the property that makes it run
unchanged on CPU:</p>
<pre><code>def score(model, tokenizer, row, metadata, max_tokens=4096):
import torch
ids, slots, prompt_hash = encode_prompt(tokenizer, row, max_tokens)
device = next(model.parameters()).device # <- follow the model
inputs = {"input_ids": torch.tensor([ids], dtype=torch.long, device=device),
"attention_mask": torch.ones((1, len(ids)), dtype=torch.long, device=device)}
if device.type == "cuda": torch.cuda.synchronize(device) # <- no-op on CPU
with torch.inference_mode():
vocabulary = _forward(model, inputs)[0].float() # (vocab,)
selected = vocabulary[slots].cpu().tolist() # logits at A, B, C, ...
return {
"option_ids": [o["id"] for o in row["options"]],
"option_logits": selected,
"probabilities": softmax(selected), # winner = argmax
"input_tokens": len(ids),
"readout": "native full-vocabulary last-position logits restricted to declared answer slots",
"probability_status": "conditional option score; uncalibrated as decision confidence",
...
}</code></pre>
<p>That is the entire "JEV-ification": a stock <code>AutoModelForCausalLM</code> is never asked to generate;
it is asked once for its next-token logits, and the decision is the arg-max over the option slots.</p>
<h3 id="a7">A.7 — The only change to run on CPU</h3>
<p>SemIf's loader hard-codes a single CUDA device and <code>bfloat16</code>. JEV-CPU replaces just this
function; every function above is untouched:</p>
<div class="tablewrap">
<table>
<thead><tr><th>SemIf — <code>core.load_causal_model</code> (GPU)</th><th>JEV-CPU — <code>load_causal_model_cpu</code></th></tr></thead>
<tbody><tr>
<td><pre style="margin:0"><code>if not torch.cuda.is_available() \
or torch.cuda.device_count() != 1:
raise ValueError("Expose exactly "
"one CUDA GPU ...")
...
model = cls.from_pretrained(
source, config=config,
dtype=torch.bfloat16,
device_map={"": "cuda:0"},
low_cpu_mem_usage=True, **common)</code></pre></td>
<td><pre style="margin:0"><code># no CUDA check, no device_map
model = AutoModelForCausalLM.from_pretrained(
source, config=config,
dtype=torch.float32, # CPU-stable
low_cpu_mem_usage=True, **common)
model.eval()</code></pre></td>
</tr></tbody>
</table>
</div>
<p>Because <code>score()</code> and <code>score_shared()</code> derive their device from the model object,
loading on CPU is sufficient — the same scoring code then runs with <code>torch.cuda.synchronize</code> skipped.
Swapping the brain model is the one other knob: change <code>MODEL = "Qwen/Qwen3-0.6B"</code> to any causal
LM whose answer letters are single tokens.</p>
<h3 id="a8">A.8 — Reusing one state across many criteria (shared mode)</h3>
<p>When many criteria judge the same <code>state</code>, <code>shared.py</code> prefills that state once into a
native key–value cache, replicates the cache across branches with <code>cache.reorder_cache(...)</code>, and
evaluates every criterion's answer position in one batched forward using a vector
<code>logits_to_keep</code>. One expensive prefill, many cheap decisions — and, like the direct path, it
synchronizes only under CUDA, so it too runs unchanged on CPU.</p>
<hr>
<h2 id="refs">References</h2>
<ol class="refs">
<li>T. Lee (TheoLeeCJ). <em>SemIf — Semantic ifs from open models.</em> <a href="https://github.com/TheoLeeCJ/SemIf">github.com/TheoLeeCJ/SemIf</a>. Browser demo: <a href="https://openjev.com/">openjev.com</a>.</li>
<li>TypeSafe. <em>Jev</em> (closed service for runtime-defined semantic decisions). Names/marks are the property of their owners; this work is independent and unaffiliated.</li>
<li>Qwen Team. <em>Qwen3-0.6B.</em> <a href="https://huggingface.co/Qwen/Qwen3-0.6B">huggingface.co/Qwen/Qwen3-0.6B</a>.</li>
<li>A. Liu et al. <em>WANLI: Worker and AI Collaboration for NLI.</em> <a href="https://huggingface.co/datasets/alisawuffles/WANLI">dataset</a> (used in SemIf's evaluation cited in §7).</li>
<li>This report and JEV-CPU code are released under the MIT License.</li>
</ol>
<footer>
JEV-CPU Technical Report · v1 · September 19, 2026 · leesk212 (Meanblock).
An independent CPU port and empirical study of <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>.
Not affiliated with or endorsed by SemIf's author, TypeSafe, or Jev.
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