vectionlabs

Salience-27B-R4

README

License: apache-2.0

Abstract

Salience 27B is a 27-billion-parameter dense vision-language model built for hard, practical engineering work: writing and debugging real code, repo-scale edits, multi-step terminal agency, and quantitative reasoning — with native vision and a 262K-token context window (extendable to 1M via YaRN).

Where the MoE tiers of the family (Pro, Flash) route a few billion active parameters per token, Salience 27B runs all 27B on every token — maximum per-token capacity, a hybrid linear+full attention stack for long-context speed, and an MTP head for self-speculative decoding.

It is 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, drive the terminal, land the pull request.

Highlights

Dense capacity. All 27B parameters active on every token — no routing, no expert misses, maximum depth on every step of a hard problem.
SWE-agent first. Tuned to produce runnable code, repo-scale edits, methodical debugging, and well-formed native tool calls.
Lives in a terminal. Plans the command sequence, checks each result before the next step, and recovers from failures instead of repeating them.
Efficient reasoning. Thinks natively before answering — and gets to the point instead of narrating: markedly fewer thinking tokens at the same answer quality.
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.
Fast decode for its size. Hybrid linear+full attention (full every 4th layer) plus an MTP head for self-speculative decoding.
Open weights. Apache-2.0, transformers-native.

Model overview

Table

Parameters	27.2B dense (all active)
Modalities	text, image, video -> text
Context window	262,144 tokens native (up to 1,048,576 via YaRN)
Attention	hybrid linear + full attention (full every 4th layer)
Decoding	MTP head included (self-speculative decoding)
Precision	bfloat16
Architecture	Qwen3.6 dense (27B) + native vision encoder
License	Apache-2.0

The family: Pro (35B-A3B MoE) · Flash (30B-A3B MoE) · 27B Preview (dense) · Nano (9B dense)

Capabilities

Code & SWE execution — runnable code, repo-scale edits, methodical debugging, robust backends.
Terminal & agentic work — 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. Reasoning is native — you never have to write think step by step (doing so makes it perform reasoning instead of doing it). Control depth with the token budget, not the prompt. 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, and by llama-server --jinja. Provide tool schemas via the chat template tools argument.

Intended use

Salience 27B R4 targets software engineering, coding agents, and technical research:

Code generation, explanation, debugging, review, and repo-scale tasks.
Terminal / tool-using agent workflows (CLI agents, browsing, ML engineering, DevOps).
Backend and systems design, infrastructure-as-code.
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

python
from transformers import AutoModelForImageTextToText, AutoProcessor
import torch

repo = "vectionlabs/Salience-27B-R4"
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.

Quantized GGUF (local)

This is a dense model, so standard quant intuition applies: Q4_K_M and up hold quality well; use Q5_K_M/Q6_K when VRAM allows. (The MoE tiers of the family need Q5/Q6 minimum — that constraint does not apply here.) Keep the MTP layers if your quant includes them: they enable self-speculative decoding for free extra speed.

Prompting tips

Let it think. No "think step by step" — reasoning is native. Budget tokens instead.
Give it the repo. 262K to 1M context: paste whole files or repos, not fragments.
Agentic loops. Use --jinja with llama-server (or vLLM/SGLang parsers) so XML tool calls become proper OpenAI-style tool_calls.
Vision mid-task. Screenshots of stack traces and UI states work as debugging inputs.

Limitations & responsible use

May hallucinate APIs or facts under ambiguity; verify critical output.
Review generated code before running it, especially anything touching production systems.
Preview build: report issues in the Community tab — they get fixed in the stable release.

Built on Qwen3.6 (Apache-2.0).

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Model Details

Model Provider

vectionlabs

Model Tree

Base

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality