entail

Your model files say how they must be run. Your engine doesn't always listen.

RoPE base and scaling, soft-capping, sliding windows, chat templates, prediction types: when one of these declarations does not reach the engine, the output is wrong without a warning. entail reads what the files already declare, checks it where it is used, repairs it before the first token when it can, and logs exactly what broke when it cannot. Zero configuration; about 1% of load time.

• 64 of 180. Of the 300 most-downloaded LLMs on Hugging Face, 180 take vLLM's launch-time rope_scaling override (the usual way to turn on long context). It silently changes the RoPE base of 64 of them. With entail on, all 180 keep their base. (vLLM 0.30, checked on the model files with vLLM's own config code; E1.)
• 379 → 273. Measured end to end, that is Llama-3.2-3B-Instruct's GSM8K score under that route (376 with entail on); Qwen3-4B-Instruct-2507 goes 183 → 175 under YaRN. No warning in either case.
• Evals miss some of it. A backend that drops Gemma 2's soft-capping changed 198 of 500 answers while GSM8K moved by 3 (p = 0.66). entail routes to a backend that honours it, at load.
• No false alarm in 102 runs. 38 popular models on transformers, vLLM and SGLang: every output identical to the run without entail, load cost median 0.7-0.9%. (1.0.0 got 17 of its first 81 runs wrong; the five causes are fixed and in the changelog.)

Check your own model in three lines:

pip install entail-ai
entail preflight --model /path/to/model --engine vllm --list   # what each backend would drop; no GPU needed
ENTAIL=load vllm serve /path/to/model ...                       # then read entail_logs/

entail makes the meaning of a value explicit, like a type: declared where it is produced and carried to where it is used; checked against the consumer's choice and against the data; resolved first when they disagree (routed to a consumer that honours it, or converted to the form the consumer reads) and, when no fix exists, reported while the run goes on (it stops only if you ask it to); said to be "unknown" when nobody declares it, instead of letting a default stand in silently. The name is the logical sense of entail: what a checkpoint declares must entail what the engine executes. (ent·AI·L: an AI library.)

Status: 1.0.1, measured on one machine. Everything below was measured on the engines and versions under Tested with, on one RTX 4070 Ti. The 1.0 evaluation is summarised under How it was measured, and what it found missing under Known gaps. entail does not look for defects inside a model, a compiler, a kernel or the hardware: when every boundary it checked held and the output is still wrong, it says so and narrows where to look.

What it does to your environment

• pip install entail-ai adds one package (import name entail, no dependencies) and one line to site-packages (entail-autoinstall.pth), which is how entail reaches the worker processes an engine starts. With ENTAIL unset that line returns at once: 0.2-0.3 ms per Python start, no module imported. entail hook status shows it and entail hook uninstall removes it.
• With ENTAIL=load, everything entail says goes to entail_logs/ in the folder the program was started from (a log and a JSON record per day, with a .gitignore). ENTAIL_LOG_DIR=off writes nothing; ENTAIL_LOG_DIR=<folder> moves it. ENTAIL_QUIET=unknown keeps the non-blocking unknown lines off the console.
• The adapters hook internal functions of the engine versions they were measured on, listed below. On another version an adapter that cannot install says so once (could not install ...) and stays out; the rest run. entail doctor prints what is installed and what would hook.

Why: a case measured end to end

Restating a model's own rope_scaling at launch — the route model cards give for enabling YaRN — drops rope_theta under transformers 5, and the engine silently falls back to a RoPE base of 10,000.

Llama-3.2-3B-Instruct, greedy, GSM8K (first 500 on vLLM, first 200 on SGLang):

The vLLM row was measured again on 1.0; the SGLang row is from the earlier measurement. The degraded runs are identical to an explicit rope_theta = 10000. Outputs stay fluent; the answers are wrong. Qwen3 dense models happen to be safe because those model files fill in 1,000,000; Llama, Qwen3-MoE, Gemma and others do not.

Install

pip install entail-ai

Install it into the same environment as your engine (vLLM, SGLang or transformers). entail has no dependencies of its own. The import name is entail. uv pip install entail-ai works the same way; for the latest commit, pip install "git+https://github.com/wwoosshh/entail".

Check what it sees:

entail doctor

Use

Turn it on with one environment variable; nothing else changes.

ENTAIL=load vllm serve meta-llama/Llama-3.2-3B-Instruct --hf-overrides '{"rope_scaling": {...}}'
ENTAIL=load python -m sglang.launch_server --model-path ... --json-model-override-args '{...}'
ENTAIL=load python your_transformers_script.py
ENTAIL=load python main.py            # ComfyUI, from its folder (on Windows: set ENTAIL=load in the launcher .bat)

When it changes something, it says so:

[entail] resolved LlamaConfig.rope_scaling was given after the config was built; it replaces rope_parameters and
would have dropped rope_theta=500000.0, ...; kept it, as config.json would

From inside a script:

import entail
entail.enable()          # mode="load", policy="resolve"; child processes inherit it

When a model file does not say what it means

Many image checkpoints declare nothing about their prediction type or latent scale, and entail then reports them as unknown instead of guessing. A manifest declares it from outside, the way a .d.ts file types a JavaScript library:

entail infer model.safetensors --out model.safetensors.entail.json   # what the file declares, and empty slots
# fill in the slots you know, then mark it reviewed:
entail pin model.safetensors.entail.json

A manifest next to the file is found by itself. Manifests kept in a folder (named <sha256>.json, the hash the draft records) are found through ENTAIL_MANIFESTS. Only a pinned manifest counts as a declaration.

How it reaches engine worker processes

vLLM and SGLang run the model in processes they start themselves. pip install puts one file, entail-autoinstall.pth, into site-packages; Python reads it at every start-up. Its single line checks the environment and does nothing unless ENTAIL is set. entail hook status|install|uninstall shows or manages it (an editable install does not place it — run entail hook install).

What it does

Resolves

| vLLM server: a tool-call parser that does not read the format the model declares (a Qwen3 model, which emits hermes calls, served with --tool-call-parser pythonic): the call comes back as plain text | switched to a parser measured to read the declared format | vLLM 0.30.0, Qwen3-4B: the tool call is returned as a structured call again | | diffusers: a VAE loaded on its own takes another model's latent scale (an SDXL VAE read as SD1.5's) | the scale a manifest declares for the model is applied | 15-16/255 from the reference image without entail, pixel-identical with it (3 seeds) |

Checks (and reports what nothing can resolve)

• attention properties against each engine's backends, before any weight is read
• config keys that would be swallowed, and tied-embedding declarations against the checkpoint
• vLLM weights after repacking: layout, stride and a value sample against the declared transform
• weights in memory against the checkpoint file (ENTAIL_SOURCE=1)
• the KV cache contract (a request holds what it needs, nothing shrank) on transformers, vLLM's paged cache and SGLang
• image models on ComfyUI and diffusers: the prediction type and latent scale a checkpoint, folder or manifest declares against the sampler and the VAE, and a LoRA's modules against the model it is applied to
• on vLLM's OpenAI server, per request: a chat template other than the declared one, reasoning history dropped where the model declares it is kept, and request fields or template settings that nothing reads
• where transformers applies a chat template - a script's apply_chat_template, SGLang's server - the template and the reasoning history the same way, and SGLang's own conversation templates (--chat-template chatml)
• in debug mode, the boundaries you declare in your own code (@entail.boundary: what each argument means); a strided layout, a quantized value and chunk-relative positions are converted where the reader needs it

entail preflight --model /path/to/model --engine sglang --list runs the start-up checks without starting a server.

When the output is still wrong

entail records a verdict for every boundary it checks, in entail_logs/. entail locate reads that record and says where meaning broke: the first boundary that did not keep it. If every boundary it checked held and the output was wrong (entail locate --wrong), the fault is not in what was handed between layers but inside one - the model, a compiler, a kernel, the hardware. A boundary entail could not check stays suspect, with the layers on either side.

To narrow it to a layer, run the code in debug mode and compare layers with a reference on the same inputs. Every layer's first call is compared (more with calls=), and a layer whose output its reference does not reproduce is named:

import torch, entail
from entail import diagnose
from transformers.models.qwen3.modeling_qwen3 import Qwen3MLP

def mlp_in_float32(self, x):   # the reference: the same MLP, computed in float32
    f = torch.nn.functional
    gate, up = (f.linear(x.float(), p.weight.float()) for p in (self.gate_proj, self.up_proj))
    return f.linear(f.silu(gate) * up, self.down_proj.weight.float()).to(x.dtype)

entail.enable("debug")         # before the model is loaded, so its load is checked too
with diagnose.propagating(), diagnose.watch(Qwen3MLP, "forward", mlp_in_float32, label="mlp"):
    model.generate(**inputs, max_new_tokens=8)
print("\n".join(entail.locate(output_wrong=True).lines()))

diagnose.propagating() also names an operation that made a declared fact untrue between two boundaries (a transpose of a value whose layout was declared). With defects planted on Qwen3-4B and gemma-2-2b-it (transformers 5.17) - at a load, cache or code boundary, inside the attention or MLP kernel, behind a boundary entail could not check - it pointed to the planted place in all 11 cases. The diagnosis cost 1.72x (eager) and 1.90x (sdpa) on a 64-token decode, with the same tokens.

In a test suite, pytest --entail runs each test that way: what breaks fails the test, and a failing test's report says where. A test that takes the entail_condition fixture, marked @pytest.mark.entail_conditions(model="..."), runs once per condition the model's declarations put at stake: one token under, at and over its sliding window, around where a scaled RoPE takes over, a second turn when it declares how earlier reasoning is kept.

For new code: a role-typed front end (experimental)

entail.frontend is for code written from scratch - a model's decode step, the caller of a kernel. Every value has a type (named dims, dtype, what it is - a query, a key, a value - and the facts it carries), every operation takes its arguments by keyword, and a program is traced once from the types of its inputs. A key passed as a value, a length used as the last key index, a weight in a format no kernel reads, a cache read in its version before a write, a sum reduced twice: each is refused before anything runs. What can be repaired - chunk-relative positions with their offset, a quantized value with its scale, a softcap the chosen kernel ignores - is repaired then, and said.

from entail.frontend import qwen3

program = qwen3.trace_decode(model.config, batch=8, slots=640, attention="triton")    # every check runs here
step = qwen3.bind(program, model, cache, tokens, positions, until)          # the load contract
logits = step()["logits"]                                                              # no checks left

Qwen3-4B's decode step written this way gives transformers' logits bit for bit with the torch lowering. Compiled and captured in a CUDA graph (int4, batch 8), it takes 1.005-1.007x the time of the same step assembled by hand with the hand kernel, and 1.020-1.024x with FlexAttention. Of 16 reproduction cases, 9 are refused or repaired while tracing and 2 more when the program is bound to its tensors; no fixed version is refused.

How it was measured

For 1.0 every measurement of the development milestones was run again on the final code, on one RTX 4070 Ti.

• Healthy runs (Qwen3-4B, Llama-3.2-3B-Instruct and gemma-2-2b-it on transformers, vLLM and SGLang, each engine's defaults): no false alarm. Two repairs, both where a backend drops Gemma 2's soft-capping. Every fact the model folders declare reached a decision where it was used, the chat template included.
• Wider healthy runs, for 1.0.1 (38 popular models that fit one 12 GB card, chosen by download rank, on the same three engines; 102 valid runs): 1.0.0 reported something wrong in 17 of the first 81 runs, none of it a real loss - the five causes are in the changelog. With 1.0.1: no broken, no refused, two repairs (Gemma 2's soft-capping again, measured rows), every output identical to the run without entail, load cost median 0.7-0.9%. What 1.0.1 cannot decide it now says as unknown (69 lines over the 81 runs, one per boundary).
• 31 test problems (16 reproduction cases, 8 field cases, 7 simulated market incidents): each defect was repaired; where no repair exists, it was reported at the boundary and fact where it happened while the run went on, or stopped with ENTAIL_ON_BROKEN=stop. No fixed version was flagged. (The two ComfyUI cases were measured before 1.0 and not run again.)
• Cost: at load, 0.3-2.6% of the load time. Always on, vLLM's CUDA-graph path 0.999-1.000x (two runs without entail: 0.997-0.999x), transformers' dynamic KV cache 1.017-1.022x of an eager decode. About 60 us per request on vLLM's server. The diagnosis mode 1.74x (eager) and 1.85x (sdpa). With ENTAIL unset, 0.2-0.3 ms per Python start and no module imported.
• Next to post-hoc detection: GSM8K (500 problems, greedy) caught the RoPE loss above and a planted weight shift, but not a backend that drops Gemma 2's soft-capping: SGLang torch_native 313 against triton 316 (McNemar p = 0.66); measured before 1.0, transformers sdpa against eager 337 against 339 (2B) and 442 against 443 (9B). Comparing the outputs with a healthy run found it (198 of 500 answers differ, none between two healthy runs), where such a run exists. entail repairs it at load.
• Locating: 11 of 11 planted defects located.

Known gaps

• The always-on KV contract on transformers' dynamic cache is at the edge of its target: 1.017-1.022x of an eager decode of Qwen3-4B, depending on how the runs are paired (target 1.02x; 1.041x before these fixes). On vLLM's CUDA-graph path it is within noise.
• A multimodal processor's apply_chat_template is not checked. A request SGLang's server refuses under ENTAIL_ON_BROKEN=stop gets SGLang's own error (500); vLLM's server answers 400.
• RoPE keys the vocabulary does not carry (yarn's beta_fast, longrope's factor lists) are reported as not compared.
• A mismatch the capability table knows only from reading an engine's code (SGLang flashinfer's sliding window) is reported as inferred, not repaired; only measured rows switch a backend. Config keys outside the vocabulary that a model's config class does not take are reported as unread, not as lost.
• SGLang's KV contract skips speculative-decoding batches (the scheduler reserves draft slots ahead of the tokens) and says so once per process.
• One GPU. The reduction contracts (a value summed twice across ranks) were measured with one process standing in for two ranks.

Configuration

Tested with

transformers 5.12.1, 5.16.1 and 5.17.0, vLLM 0.30.0, SGLang 0.5.20, diffusers 0.40.0, ComfyUI 0.34.1 (Windows), torch 2.13–2.14, Python 3.12, one RTX 4070 Ti (12 GB). Other versions may work; entail doctor prints what is installed. On transformers 4.x the RoPE resolver has nothing to do and stays out of the way.

The capability table (which backend honours what) records its evidence per entry, and only entries marked measured are used as resolution targets. The measurement scripts, the raw results and the documents this repository's code refers to (THEORY.md, LIBRARY_DESIGN.md, ROADMAP.md) are published as the research workspace at https://github.com/wwoosshh/entail-research (mostly in Korean; the numbers in this README are traced to result files there, see its testbed/results/m10/PUBLIC_CLAIMS.md).

Development

git clone https://github.com/wwoosshh/entail && cd entail
pip install -e .
entail hook install          # editable installs do not place the start-up hook
PYTHON=python bash tests/run_all.sh

Tests that need a local model look in ENTAIL_TEST_MODELS (default ~/models) and skip when it is absent.

한국어 안내: README.ko.md

License

See LICENSE.