Instructions to use LiamVisionary/swarm-sovereign-scout-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiamVisionary/swarm-sovereign-scout-12b with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiamVisionary/swarm-sovereign-scout-12b")
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("LiamVisionary/swarm-sovereign-scout-12b")
model = AutoModelForMultimodalLM.from_pretrained("LiamVisionary/swarm-sovereign-scout-12b", 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 LiamVisionary/swarm-sovereign-scout-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiamVisionary/swarm-sovereign-scout-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiamVisionary/swarm-sovereign-scout-12b",
		"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/LiamVisionary/swarm-sovereign-scout-12b

SGLang

How to use LiamVisionary/swarm-sovereign-scout-12b 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 "LiamVisionary/swarm-sovereign-scout-12b" \
    --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": "LiamVisionary/swarm-sovereign-scout-12b",
		"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 "LiamVisionary/swarm-sovereign-scout-12b" \
        --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": "LiamVisionary/swarm-sovereign-scout-12b",
		"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 LiamVisionary/swarm-sovereign-scout-12b with Docker Model Runner:
```
docker model run hf.co/LiamVisionary/swarm-sovereign-scout-12b
```

Swarm Scout 12B

Swarm Scout mascot

Swarm Scout 12B is a low-refusal coding/agentic fine-tune derived from yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2, which is based on google/gemma-4-12B-it.

The upstream base is specialized for coding, terminal workflows, native Gemma 4 tool-use, multi-step technical agent tasks, and reasoning. This version applies a local Heretic/abliteration pass targeting reduced opening-refusal behavior.

Brand note: this release is now named Swarm Scout. The Hugging Face repo slugs and GGUF filenames still contain swarm-sovereign-scout so existing downloads, Modal caches, and copied links keep working.

Build With HivemindOS

Want to build and manage an entire private swarm of agents? HivemindOS gives you shared memory, reusable skills, one-time setup, and a superbrain architecture for coordinating agent work.

App: HivemindOS
X: @TheHivemindOS

Strengths

Coding and debugging assistance.
Terminal-style agent workflows: read, reason, act, verify.
Native Gemma 4 tool-use protocol.
Reasoning-heavy technical work with configurable thinking.
Local/autonomous agent loops where a low-refusal assistant is desired.

The upstream model card reports a strong relative gain on local tau2-bench telecom-style agentic tool-use compared with google/gemma-4-12B-it; see the upstream card for methodology and caveats.

Abliteration / Refusal Check

Local verified artifact:

Winning Heretic trial: 20
Heretic proxy refusals: 0/6
KL divergence: 0.008679866790771484
Merged HF strict refusal check: 0/6 strict full-response refusals
Served Q4_K_M llama.cpp strict refusal check: 0/6 strict full-response refusals

The strict check measures refusal behavior on a small local prompt set. It is not a complete safety, misuse, or quality benchmark. Add application-level guardrails where needed.

Modal / OpenAI-Compatible Endpoint

The verified hosted endpoint for this release serves the companion GGUF repo through llama.cpp on Modal as an OpenAI-compatible API.

Recommended served model name:

swarm-scout-12b

The hosted Modal proxy accepts the previous swarm-sovereign-scout-12b alias for compatibility, but new clients should request swarm-scout-12b.

The deployment uses the companion GGUF artifacts:

LiamVisionary/swarm-sovereign-scout-12b-GGUF
swarm-sovereign-scout-Q4_K_M.gguf
MTP/gemma-4-12B-it-MTP-Q8_0.gguf
mmproj-swarm-sovereign-scout-bf16.gguf
chat_template.jinja

Verified Modal serving path:

Runtime: llama.cpp server-cuda-b9870
GPU: Modal A10
Draft type: draft-mtp
Draft settings: --spec-draft-n-min 0 --spec-draft-n-max 2
Multimodal projector: mmproj-swarm-sovereign-scout-bf16.gguf
Image input verified through OpenAI-compatible chat content parts
Hosted endpoint auth: Modal proxy auth is enabled. Server-side callers must send Modal proxy headers; do not expose those credentials in browser/mobile clients.

vLLM was tested first, but the available vLLM path did not load this Gemma 4 unified/assistant combination reliably. Use the GGUF/llama.cpp route unless vLLM adds compatible support for this model family.

Local Transformers Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "LiamVisionary/swarm-sovereign-scout-12b"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a small Python LRU cache."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Use a recent transformers build with Gemma 4 unified architecture support.

GGUF

The local/runtime GGUF release is published separately at:

LiamVisionary/swarm-sovereign-scout-12b-GGUF

That repo includes:

swarm-sovereign-scout-Q4_K_M.gguf
MTP/gemma-4-12B-it-MTP-Q8_0.gguf
mmproj-swarm-sovereign-scout-bf16.gguf
chat_template.jinja

Local llama.cpp MTP speculative decoding was verified with llama.cpp b9553 and --spec-draft-n-min 0 --spec-draft-n-max 2. Modal serving was verified with llama.cpp server-cuda-b9870.

Multimodal Status

The underlying Gemma 4 unified configuration includes image/video/audio processor metadata. The companion GGUF release now includes a tested llama.cpp projector artifact:

mmproj-swarm-sovereign-scout-bf16.gguf

Verified on 2026-07-05:

Local llama.cpp b9553 loaded the Q4_K_M GGUF plus the projector.
Modal llama.cpp server-cuda-b9870 advertised multimodal capability.
Production image request returned Merchant: SWARM CAFE. Total: $8.37. from a receipt image.

Image input is verified. The projector metadata also exposes an audio encoder, but audio/video input was not separately quality-tested for this release.

Caveats

Specialized for coding, terminal, tool-use, and local agentic work.
General-knowledge answers should be checked.
Low-refusal behavior is intentional; production applications should add policy and abuse-prevention layers.
English-centric.
Shared as-is, with no warranty.

Lineage

Base model: google/gemma-4-12B-it
Agentic/coding base: yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2
GGUF/MTP reference repo: yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF

Released under Apache 2.0, following the upstream/base lineage.

Downloads last month: 54

Safetensors

Model size

12B params

Tensor type

F16

Model tree for LiamVisionary/swarm-sovereign-scout-12b

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

(134)

this model