We know Jev.

jevchat turns that into a chat model. At every step it asks Jev one question:

Given the user's question and the reply written so far, which symbol comes next?

The options are an alphabet plus an option to stop emitting. Jev returns a probability for each one, and the sampler draws the next symbol from that normalised distribution. Append, repeat, and stop when STOP is drawn.

There are several alphabets and sampling strategies available.

The idea is for fun, the cost is somewhat impractical, and the results are hilarious.

This was a Claude accelerated experiment. I described the sampling algorithms, strategies, and so on, and it implemented them.

Put your Jev key in .env next to pyproject.toml (git-ignored):

JEV_API_KEY and TYPESAFE_API_KEY are also accepted. Values in .env win over exported ones, so editing the file is enough to switch keys.

poetry run jevchat                          # interactive chat
poetry run jevchat ask "do people need water?"
poetry run jevchat alphabets                # what you can sample from
poetry run jevchat bench                    # compare every mode (table below)

The reply appears as it is sampled, in a panel with a live readout of the generation rate — symbols/s, characters/s, milliseconds per API call, elapsed time — and the top few symbols Jev scored at the last step, so you can watch the distribution the sampler is drawing from.

Ctrl-C cancels. The first press stops generation once the in-flight request returns and keeps the partial reply; a second press aborts immediately. In chat, the partial reply stays in the conversation history. ask exits 130 when cancelled.

Chat commands: /help, /alphabet [name], /temp <v>, /stop-bias <v>, /reset, /stats, /exit.

Two things are swappable: how the distribution over the next symbol is obtained (-s/--strategy), and what it is over (-a/--alphabet). Every combination below is a runnable command.

choice asks one question over the whole alphabet. bisect sorts the alphabet and asks earlier/later yes-no questions until the group is small, then asks one choice question inside it.

# choice — one question over the whole alphabet (the default)
poetry run jevchat -s choice ask "how many eyes do people have?"

# ...without the re-ordering that cancels Jev's position bias (worst mode)
poetry run jevchat -s choice --no-shuffle-criteria ask "how many eyes do people have?"

# ...averaging 4 re-orderings, sent as 4 parallel questions in one request
poetry run jevchat -s choice --ensemble 4 ask "how many eyes do people have?"

# bisect — earlier/later down to groups of 20, each split asked both ways
poetry run jevchat -s bisect ask "how many eyes do people have?"

# ...cheaper: bigger groups, each split asked once
poetry run jevchat -s bisect --bisect-cutoff 32 --no-bisect-swap ask "how many eyes do people have?"

# buckets — the alphabet split across many questions, each with an OTHER escape.
# The only strategy that can hold more than 255 symbols.
poetry run jevchat -a words1k -s buckets ask "what colour is snow?"
poetry run jevchat -a bpe5k -s buckets --bucket-size 127 ask "what is the capital of france?"

# refine — buckets, then a question over the winners, then a rescored nucleus.
# Twice the probability on the right symbol and ~19x the vocabulary resolved.
poetry run jevchat -a words1k -s refine ask "where do fish live?"
poetry run jevchat -a words1k -s refine --refine-nucleus 6 --refine-rounds 2 ask "…"

# hypothesis — options are the resulting texts (the default)
poetry run jevchat -p hypothesis --window 40 ask "what colour is snow?"

# symbol — options are the bare symbols, as the first version of this did
poetry run jevchat -p symbol ask "what colour is snow?"

# keep 3 candidate replies alive instead of committing symbol by symbol
poetry run jevchat -b 3 ask "what is the opposite of hot?"

Costs one score per live beam per step. Above width 1, temperature, top_p and top_k stop applying — beams are ranked by probability, not drawn from.

poetry run jevchat -a lower26 -t 0 ask "what is 2+2?"   # a-z and space only
poetry run jevchat -a ascii   -t 0 ask "what is 2+2?"   # spells anything
poetry run jevchat -a tokens  -t 0 ask "do people need water?"   # whole words

# these three exceed 255 options, so they need --strategy buckets
poetry run jevchat -a words1k -s buckets -t 0 ask "what colour is grass?"
poetry run jevchat -a bpe2k   -s buckets -t 0 ask "where do fish live?"
poetry run jevchat -a bpe5k   -s buckets -t 0 ask "what do bees make?"

poetry run jevchat -a tokens -s bisect --bisect-cutoff 20 ask "do people need water?"
poetry run jevchat -a ascii -s choice --ensemble 12 -t 0.2 --repetition-penalty 1.0 \
    ask "what colour is grass?"

There are two ways to ask Jev the same question. Under --presentation symbol the options are the symbols themselves — 'a', 'i', ' the' — and Jev has to append the option to the reply in its head before judging it. The instructions used to say exactly that: "judge grammar and spelling on the concatenation, not on the option on its own."

Under --presentation hypothesis the options are the resulting texts:

answer_so_far = "The capital of France is Par"

symbol      options:  'a'  'i'  's'  …  STOP
hypothesis  options:  '…he capital of France is Para'
                      '…he capital of France is Pari'
                      '…he capital of France is Pars'
                      '…he capital of France is Par'     <- unchanged: this is STOP

The append is already done, so Jev only ranks finished strings — which is what a decision model is built for. It is the single largest improvement in the project: on character alphabets it roughly triples top-1 and doubles the probability mass landing on the right symbol, for fewer input tokens than symbol options with their per-option descriptions.

158 tests, all offline — a scripted fake client for the generation loop and an httpx.MockTransport for the HTTP layer. No API key and no network needed. jevchat bench is the part that does hit the API.