vectionlabs/Salience-1.5-Nano

Salience 1.5 — Nano

The 9B that finishes the job.

Vection Labs

Weights · Pro · Flash

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Most small models can talk about your codebase.

Nano works inside it.

Nine billion parameters, dense. Native vision. Native reasoning. A quarter-million tokens of context. Small enough to sit on one consumer GPU and stay there — and built, like everything in the Salience family, around a single question: did the task actually get done?

What it does

Ships patches, not suggestions. Reads before it writes. Finds the cause instead of the symptom, then changes the smallest thing that fixes it.

Lives in a terminal. Plans the sequence, checks the result of each step before taking the next, and recovers from a failure rather than repeating it.

Sees what you see. Screenshots, diagrams, mockups, stack traces in an image — first-class input, not an afterthought.

Thinks without being told to. Reasoning is native and on by default. You never have to write think step by step — and you should not: this model reasons on its own, and asking makes it perform instead.

The family

Pro	35B-A3B	the heavy tier
Flash	30B-A3B	multimodal, long horizon
Nano	9B dense	runs on your machine

Start

from transformers import AutoModelForImageTextToText, AutoTokenizer

repo  = "vectionlabs/Salience-1.5-Nano"
tok   = AutoTokenizer.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(repo, dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Find and fix the off-by-one in this loop: ..."}]
text   = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out    = model.generate(**inputs, max_new_tokens=2048)
print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))

~19 GB in fp16, ~6.5 GB quantized. Pass enable_thinking=False when you want the answer and not the reasoning.

Specifications

Parameters	9.65B dense
Modalities	text, image, video to text
Context	262,144 tokens
Precision	bfloat16
License	Apache-2.0

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_{Nano is small: it will lose to frontier-scale models on the hardest problems, and it can be confidently wrong — verify what matters. Review generated code before running it. Built on Qwen3.5 (Apache-2.0).}

_{© 2026 Vection Labs}