Instructions to use singularity-layer/Singularity-xMESH-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use singularity-layer/Singularity-xMESH-0.8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="singularity-layer/Singularity-xMESH-0.8B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("singularity-layer/Singularity-xMESH-0.8B")
model = AutoModelForMultimodalLM.from_pretrained("singularity-layer/Singularity-xMESH-0.8B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use singularity-layer/Singularity-xMESH-0.8B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf singularity-layer/Singularity-xMESH-0.8B:BF16
# Run inference directly in the terminal:
llama cli -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf singularity-layer/Singularity-xMESH-0.8B:BF16
# Run inference directly in the terminal:
llama cli -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf singularity-layer/Singularity-xMESH-0.8B:BF16
# Run inference directly in the terminal:
./llama-cli -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf singularity-layer/Singularity-xMESH-0.8B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Use Docker

docker model run hf.co/singularity-layer/Singularity-xMESH-0.8B:BF16

Llama new
LM Studio
Jan

vLLM

How to use singularity-layer/Singularity-xMESH-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "singularity-layer/Singularity-xMESH-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "singularity-layer/Singularity-xMESH-0.8B",
		"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/singularity-layer/Singularity-xMESH-0.8B:BF16

SGLang

How to use singularity-layer/Singularity-xMESH-0.8B 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 "singularity-layer/Singularity-xMESH-0.8B" \
    --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": "singularity-layer/Singularity-xMESH-0.8B",
		"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 "singularity-layer/Singularity-xMESH-0.8B" \
        --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": "singularity-layer/Singularity-xMESH-0.8B",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use singularity-layer/Singularity-xMESH-0.8B with Ollama:
```
ollama run hf.co/singularity-layer/Singularity-xMESH-0.8B:BF16
```
Unsloth Desktop

How to use singularity-layer/Singularity-xMESH-0.8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "singularity-layer/Singularity-xMESH-0.8B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use singularity-layer/Singularity-xMESH-0.8B with Docker Model Runner:
```
docker model run hf.co/singularity-layer/Singularity-xMESH-0.8B:BF16
```

Lemonade

How to use singularity-layer/Singularity-xMESH-0.8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull singularity-layer/Singularity-xMESH-0.8B:BF16

Run and chat with the model

lemonade run user.Singularity-xMESH-0.8B-BF16

List all available models

lemonade list

Hermes Agent

How to use singularity-layer/Singularity-xMESH-0.8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default singularity-layer/Singularity-xMESH-0.8B:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use singularity-layer/Singularity-xMESH-0.8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singularity-layer/Singularity-xMESH-0.8B:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "singularity-layer/Singularity-xMESH-0.8B:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Singularity-xMESH-0.8B / PORTABILITY.md

ivaavi

Verify xMESH on Apple Metal and NVIDIA Vulkan GPUs

d20f5ff verified 10 days ago

preview code

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3.11 kB

xMESH portability contract

Singularity Node must serve xMESH through the same /v1/systemone request and response contract on every supported platform.

Runtime

Minimum llama.cpp: b11719 (5aeac37a35b44fde4bdffaec9159f2fcb253b622).
Context: 2,048 tokens per request.
Physical batch: 2,048 tokens. The llama.cpp default of 512 rejects normal multi-field requests.
Default concurrency: one slot. Higher concurrency requires a platform-specific canary.
GPU offload (-ngl 99) on accelerators that passed frozen-suite decision parity against CPU: Apple Metal (959/960 decisions, one near-tie) and NVIDIA through the Vulkan build (960/960). Singularity Node v1.10.4+ selects this itself. Every other setup runs on CPU (-ngl 0). Evidence: ACCELERATOR_PARITY.json.
System memory: 8 GB minimum; 16 GB recommended.

Artifacts

Artifact	Bytes	SHA-256	Use
`Singularity-xMESH-0.8B-BF16.gguf`	1,571,529,600	`9f4c08f19c4d203059bc74ed2754e800ec472d7276d8ddb2321480e86d392fff`	Universal Node artifact
`Singularity-xMESH-0.8B-Q8_0.gguf`	841,069,440	`41b36270b67f6c595e98cf4f29d6a0d13f0c19daa37b75741cd8296e2729fd75`	Accelerated runtimes only

Q8 passes the full Metal decision-parity suite but crashes in llama.cpp b11719 during CPU initialization. Q4_K_M loads but changes 16 of 960 frozen decisions, so it is not a release artifact.

Release matrix

Surface	Target	Artifact	Status
Desktop	macOS Apple silicon	BF16	Full Metal offload: 959/960 frozen-suite decisions match CPU; packaged-app Run Locally and Grid serving canaries passed (Desktop 1.10.7)
Desktop / Node	Windows x64 CPU	BF16	Production Vulkan distribution with `-ngl 0` passed 9/9 decisions
Node CLI	Linux x64 CPU	BF16	Production Vulkan distribution with `-ngl 0` passed 9/9 decisions on Ubuntu 24.04
Node CLI	Linux ARM64 CPU	BF16	CPU load and real decision canary passed in Ubuntu 24.04 ARM64
Node CLI	Linux x64 NVIDIA GPU	BF16	Production Vulkan distribution with `-ngl 99` on an RTX A4000: 960/960 frozen-suite decisions match CPU, 0.31 s median per request

The desktop release workflow does not currently publish Linux or Intel Mac packages. The Node CLI covers Linux; Intel Mac desktop support requires a separate release target.

The official Linux llama.cpp archive links libgomp.so.1. Minimal Ubuntu images must install the standard libgomp1 package before sgl setup; normal desktop/server distributions often already include it. The provisioner must surface this exact prerequisite when the staged binary fails its launch check.

Quality gates

Frozen x402 export parity: 80 cases and 960 decisions.
Official typed-decisions boundary suite: 400 cases and 2,000 decisions.
Option-order robustness: 600 choice decisions.
Runtime integrity: exact artifact and llama.cpp archive hashes.
Platform canary: load, health, real /v1/systemone request, expected answer, and clean shutdown.

See GGUF_VALIDATION.json for measurements and rejected artifact evidence.