Instructions to use vectionlabs/Salience-1.5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vectionlabs/Salience-1.5-Pro with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="vectionlabs/Salience-1.5-Pro")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vectionlabs/Salience-1.5-Pro")
model = AutoModelForMultimodalLM.from_pretrained("vectionlabs/Salience-1.5-Pro", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vectionlabs/Salience-1.5-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vectionlabs/Salience-1.5-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vectionlabs/Salience-1.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vectionlabs/Salience-1.5-Pro

SGLang

How to use vectionlabs/Salience-1.5-Pro with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vectionlabs/Salience-1.5-Pro" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vectionlabs/Salience-1.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vectionlabs/Salience-1.5-Pro" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vectionlabs/Salience-1.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use vectionlabs/Salience-1.5-Pro with Docker Model Runner:
```
docker model run hf.co/vectionlabs/Salience-1.5-Pro
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Salience 1.5 — Pro

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

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


Parameters	35B total / ~3B active (256 experts, 8 routed + 1 shared)
Modalities	text, image, video -> text
Context window	262,144 tokens native
Attention	hybrid linear + full attention (full every 4th layer)
Precision	bfloat16 (float32 SSM components preserved)
Architecture	Qwen3.5 MoE (35B-A3B) + native vision encoder
License	Apache-2.0
Library	🤗 `transformers` (`AutoModelForImageTextToText`)

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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Safetensors

Model size

36B params

Tensor type

BF16

F32

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Quantizations

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