Salience-1.5-Pro

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Salience 1.5 — Pro

<p align="center"> <img src="Salience-1.5-Pro-Banner.png" alt="Vection Labs Salience 1.5 Pro Banner" width="100%"> </p>

A 35B-A3B Mixture-of-Experts software-engineering agent — only ~3B active params per token: the decode speed of a small model with the reach of a large one.

Vection Labs

Weights · Quickstart · Limitations

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Abstract

Salience 1.5 Pro is a sparse Mixture-of-Experts vision-language model: 35B total parameters, but only ~3B active per token across 256 experts (8 routed + 1 shared). It decodes at the speed of a small dense model while reasoning with the capacity of a 35B one — built for hard, practical engineering work: writing and debugging real code, repo-scale edits, robust backend systems, and driving tools and agents through long-horizon tasks, with native vision and a 262K-token context window.

It is the flagship tier of the Salience family — engineered for people who care less about chat pleasantries and more about whether the model can do the thing: ship the function, find the bug, call the right tool, land the pull request.

Highlights

• Bigger and faster at once. Sparse activation means ~3B of compute for 35B of knowledge — and a hybrid linear+full attention stack keeps long contexts fast.
• SWE-agent first. Tuned to produce runnable code, repo-scale edits, methodical debugging, and well-formed native tool calls.
• Long-horizon agentic execution. Multi-step planning and tool orchestration that hold up across extended task chains.
• Reasoning that shows its work. Structured, inspectable chains of thought — with a one-token switch to turn them off when you want instant answers.
• Genuinely multimodal. Images and video are first-class inputs — read a diagram, a UI screenshot, a stack-trace screenshot, or a whiteboard photo mid-task.
• Long context. 262,144 tokens native — whole repos and long specifications in a single prompt.
• Open weights. Apache-2.0, transformers-native, single-file deployment.

Model overview

Multimodal MoE sibling: Salience-1.5-Flash (30B-A3B, vision + video + 1M context).

Capabilities

Salience 1.5 Pro routes only ~3B parameters per token through a 35B expert network. Its capability profile is built around four pillars:

• Code & SWE execution - runnable code, repo-scale edits, methodical debugging, robust backends.
• Agentic tool-use - multi-step planning, tool orchestration, long-horizon task execution.
• Deep reasoning - structured, inspectable chains of thought for hard, multi-step problems.
• Multimodal perception - diagrams, screenshots, documents, and video as first-class inputs.

Thinking

Thinking is on by default: the model reasons inside <think>...</think> before answering, and serving stacks expose it as reasoning_content. Pass enable_thinking=False to apply_chat_template for instant direct answers.

Tool calling

The model emits XML-style tool calls (<tool_call><function=...><parameter=...>), parsed natively by vLLM / SGLang tool parsers for this model family. Provide tool schemas via the chat template tools argument.

Intended use

Salience 1.5 Pro targets software engineering, coding agents, and technical research:

• Code generation, explanation, debugging, review, and repo-scale tasks.
• Agentic / tool-using workflows (terminal agents, browsing, ML engineering).
• Backend and systems design.
• Step-by-step reasoning and quantitative problem solving.
• Screenshot / diagram / document understanding inside engineering workflows.

It is not intended for high-stakes decisions without human review, nor as a source of truth for medical, legal, or financial advice.

Quickstart

from transformers import AutoModelForImageTextToText, AutoProcessor
import torch

repo = "vectionlabs/Salience-1.5-Pro"
proc = AutoProcessor.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(
    repo, dtype="auto", device_map="auto"
)

messages = [{
    "role": "user",
    "content": [{"type": "text", "text": "Implement an LRU cache in Python with O(1) get/put."}],
}]
text = proc.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = proc(text=[text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(proc.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

Requires a recent transformers (>= 5.8). Vision works the same way with {"type": "image", "image": ...} content items. Pass enable_thinking=False to apply_chat_template for direct answers.

Quantized GGUF (recommended for local)

This is a sparse MoE (~3B active). Use Q6_K minimum (Q5_K_M acceptable) - Q4 and below corrupt the router and cause loops / degeneration. Dense-model Q4 intuition does not apply here.

Prompting tips

• Code: specify language, constraints ("no external libraries"), and the exact I/O contract.
• Agentic / tools: pass real tool schemas via the template; the model emits XML-style calls.
• Reasoning: thinking is on by default; let it externalize its work on hard problems.
• Vision: put the image before the question in the message content.
• Sampling: temperature=0.6-0.85, top_p=0.95, top_k=20, presence_penalty=1.1 (lower temperature for precise code edits, higher for long agentic sessions).

Deployment

• Single / multi GPU: loads with device_map="auto"; keep dtype="auto" so float32 components stay float32.
• Serving: vLLM / SGLang with the model family's reasoning parser (<think> -> reasoning_content) and XML tool-call parser.
• Quantized formats: GGUF and other community quantizations are supported (Q6_K+ for MoE).

Limitations & responsible use

• Salience 1.5 Pro can be confidently wrong. Verify factual and mathematical claims.
• Generated code may be insecure or incorrect - review before running, never execute untrusted output.
• Long-context and video inputs increase latency and memory substantially.
• Do not use it for surveillance, manipulation, or any use that violates applicable law or the Apache-2.0 terms.
• No audio modality.

Citation

@misc{vectionlabs2026salience15pro,
  title  = {Salience 1.5 Pro: A Sparse-MoE Software-Engineering Agent},
  author = {Vection Labs},
  year   = {2026},
  url    = {https://huggingface.co/vectionlabs/Salience-1.5-Pro}
}

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<div align="center">_{(c) 2026 Vection Labs - Apache-2.0}</div>