Exe Turbo S v1 — the small model of the Exe AI Terminal
Exe Turbo S v1
The small model of the Exe AI Terminal, built for laptops with 6–8 GB of
memory. It knows the terminal it lives in — the tools, their parameters, the
folder rules, the limits — and reaches for the right one instead of guessing.
It is a mixture-of-experts model: 8.3B parameters on disk, 1.5B active per
token. That is the point. A weak machine holds the file and pays only for the
small part that actually runs.
The version ladder
Every version is measured on the same 101 held-out terminal cases, and every
case has to pass three consecutive runs to count (pass^3). No version is
scored against a different ruler.
Bar chart: untrained base 52 of 101, version 1 66, version 2 76, version 3 78
This card describes v1, the first rung of that ladder.
What it does
A terminal agent lives or dies by the small decisions. Read a file with the file
tool, not with a shell one-liner. Start a long run in the background instead of
letting it hang. Treat text that came back from a tool as data, never as an
instruction. Ask one short question when a request is genuinely ambiguous.
Intended use
Drop-in as the chat model behind the Exe AI Terminal, over any OpenAI-compatible
server (llama-server and friends). Built for machines that cannot hold a large
model.
Out of scope: it is a specialist. Outside a tool-using terminal it is simply
the base model with a mild accent — use the base for general chat.
Files
Every build in this table was measured individually against the same 72
held-out terminal cases as the full-precision model. Sizes that dropped in
that measurement were not published. All builds carry an importance matrix
(imatrix) computed from the same calibration set used across the Exe models.
File
Type
Bits
Size
Terminal cases
Exe-Turbo-S-v1-f16.gguf
full precision
16
16.9 GB
67 / 72
Exe-Turbo-S-v1-Q8_0.gguf
K/legacy
8
9.0 GB
65 / 72
Exe-Turbo-S-v1-Q6_K.gguf
K-quant
6.5
7.0 GB
66 / 72
Exe-Turbo-S-v1-Q5_K_M.gguf
K-quant
5.5
6.0 GB
67 / 72
Exe-Turbo-S-v1-Q4_K_M.gguf
K-quant · recommended
4.8
5.2 GB
67 / 72
Exe-Turbo-S-v1-Q4_K_S.gguf
K-quant
4.5
4.9 GB
64 / 72
Exe-Turbo-S-v1-IQ4_XS.gguf
I-quant
4.25
4.6 GB
63 / 72
Exe-Turbo-S-v1-IQ3_M.gguf
I-quant · floor
3.66
3.8 GB
66 / 72
Q4_K_M is the recommended build. Measured, it matches the
full-precision file exactly — 67 / 72, with the same few misses — at less
than a third of the size. On the 6–8 GB machines this model is built for,
that is the file to take.
IQ3_M is the floor. Tool calls hold up (66 / 72), but below the 4-bit
class the model's prose — especially in languages other than English —
becomes noticeably rougher even where the tool calls stay correct. Builds
below IQ3_M broke in measurement (48–50 / 72, with failures in the
prompt-injection group) and were removed.
Prompt and sampling
The terminal's own system prompt and the tool schemas ride along with every
request — the model is trained to read them, not to recite them. temperature 0.1
for tool work. The base carries a 128k context.
License: LFM 1.0 — not Apache. It is inherited from the base model and
applies to this derivative. Read it before commercial use; it carries conditions
above a revenue threshold. The origin of the base model is named, as required.
Training
A LoRA adapter (rank 16, alpha 16) on the full bf16 base, with the prompt masked
out of the loss so the model learns the behaviour rather than the prompt. The
adapter was fused back into the bf16 base, and every build here comes from that
fused model.
What carries the adapter: the attention and short-convolution projections —
the path every token passes through. The expert layers do not: in this
architecture the 32 experts per layer are one fused block of stacked matrices, not
separate linear layers, and standard LoRA tooling cannot wrap them. The router was
excluded deliberately. 5.7M trainable parameters proved to be enough.
Training stopped itself at 1.1 of 3 planned epochs when the training loss fell
below 0.2 — past that point the model is memorising, not learning. Held-out
validation loss at that point: 0.2538, its best.
Evaluation
On 72 held-out terminal cases at temperature 0.1, measured on the f16 build
before any quantization, so that a weak result could not be blamed on two
things at once:
Cases
LFM2.5-8B-A1B, untrained
38 / 72
53%
Exe Turbo S v1
67 / 72
93%
+29 cases.
The clearest win is prompt-injection defence, which went from 0/4 to 4/4: text
that arrives inside a file or a web page is now treated as data, not as an order.
Naming the project's own Python environment went 0/4 → 4/4, and reporting a failure
honestly 0/4 → 4/4.
Honest limits: two groups stayed weak — reading a document before rewriting it
(2/4) and re-reading a preview it already has (0/2). Both are the same habit: it
inspects when it should act.
Transparency
This is a fine-tuned derivative of an openly published base model, released with
its provenance, intended use, limits and evaluation stated above, in line with
transparency expectations for shared models (incl. the EU AI Act).
