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

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

x402 Multi-Endpoint Selection Head

Trained and evaluated with Singularity Training Lab.

Release article · Singularity Layer

Singularity xMESH 0.8B is a compact decision model for selecting services and composing constrained multi-step x402 flows. This repository contains the complete standalone model, its calibrated joint decision head, and the deterministic xMESH policy. No separate base-model download is required.

Intended behavior

Input: an intent, hard constraints, and a small candidate set retrieved from a current x402 catalog. The validated runtime contract is 2,048 tokens.

Output: candidate selection or abstention, calibrated confidence, approval requirement, network compatibility, and cost level. Deterministic policy validates stage gates and flow feasibility before any paid request. Service choices remain model decisions.

Training data

The general decision warm-up used licensed rows from MaziyarPanahi/AgentToolDecisions-180K. That dataset retains mixed CC BY 4.0, Apache-2.0, and MIT source terms per row; see THIRD_PARTY_NOTICES.md. Singularity-curated x402 rows supplied the routing specialization. Frozen evaluation cases and resources were excluded from training.

Hardware compatibility

Hardware	Status
Apple silicon MPS	Verified on an M4 with 16 GB unified memory
CPU	Verified; portable but slower
llama.cpp Metal	Verified with BF16 full offload on an Apple M4: 959/960 frozen-suite decisions match CPU (one near-tie). Q8 also passes 960/960
llama.cpp Windows/Linux x64 CPU	Verified; native canaries passed 9/9 expected decisions on both platforms
llama.cpp Vulkan on NVIDIA	Verified on an RTX A4000: 960/960 frozen-suite decisions match CPU, 0.31 s median per request
NVIDIA CUDA	PyTorch runtime support; the llama.cpp CUDA build is not yet verified
AMD and Intel GPUs	Not verified; Singularity Node runs xMESH on CPU there

Singularity Node v1.10.4 and later choose the path automatically: full GPU offload on Apple silicon and on NVIDIA with the Vulkan build, CPU everywhere else. Full results: ACCELERATOR_PARITY.json.

The checkpoint contains 873M BF16 parameters. An 8 GB system is the practical minimum; 16 GB is recommended for comfortable local loading and inference.

Portable GGUF runtime

Singularity-xMESH-0.8B-BF16.gguf is the universal Singularity Node artifact. It is 1,571,529,600 bytes with SHA-256 9f4c08f19c4d203059bc74ed2754e800ec472d7276d8ddb2321480e86d392fff and requires llama.cpp b11719 or newer for native Clef /v1/systemone support.

Singularity-xMESH-0.8B-Q8_0.gguf is an optional 841,069,440-byte accelerated artifact with SHA-256 41b36270b67f6c595e98cf4f29d6a0d13f0c19daa37b75741cd8296e2729fd75. It preserved all 960 decisions across the frozen 80-case Metal export-parity run, but llama.cpp b11719 crashes while initializing this quantized Clef architecture on CPU. Singularity Node therefore uses BF16 for its cross-platform contract. Q4_K_M was rejected because it changed 16 decisions and preserved only 65 of 80 complete flows.

Frozen x402 regression evaluation

The standalone export was fixed before one evaluation on the existing frozen 80-case x402 suite. No weights, prompts, or policy rules were changed after seeing these results.

Evaluation, 80 cases	xMESH standalone	Jev	Laya
Complete-flow exact match	80.0%	11.25%	0.0%
Stage-gate accuracy	96.25%	86.5%	78.5%
Active-service accuracy	97.5%	100.0%	85.63%
Global-decision accuracy	96.25%	37.5%	35.63%
Executed cost regret	$0.00175	$0.00000	$0.00358
Post-policy violations	0	—	—

Jev is a hosted API and Laya used a different local runtime, so latency is not a hardware-equivalent comparison. The standalone CPU validation measured 6.45 seconds median per full multi-field flow on an Apple M4. Optimized serving runtimes can be substantially faster.

General typed-decision evaluation

This is a boundary test, not the model's specialization. xMESH was evaluated zero-shot on LocalLLaMA/typed-decisions revision 7189f46b02248bd2569df7325c9df8e3b24e90a7, complete all/test split: 400 cases and 2,000 decisions.

Model	Condition	Accuracy	Soft accuracy	Brier ↓	ECE ↓	Score MAE ↓
xMESH 0.8B	zero-shot Q8	0.478	0.415	0.409	0.293	0.642
Laya	base checkpoint, published	0.362	0.331	0.315	0.174	0.694
Laya typed-decisions	trained on this benchmark's train split, published	0.766	0.471	0.061	0.213	0.242
Jev 1.13	published	0.727	0.580	0.148	0.144	0.391

xMESH beats base Laya on this suite, but it does not beat Jev or the benchmark-specific Laya fine-tune. Accuracy by xMESH workflow was 0.266 agent-trace observability, 0.578 customer service, 0.492 invoice processing, and 0.574 security incidents.

A seeded option-order permutation changed 0 of 600 choice answers and produced zero probability drift because the Clef runtime canonicalizes option identifiers before inference.

On a short fixed state with identical three-option questions, Q8 llama.cpp on an Apple M4 measured 168 ms p50 for one question, 448 ms for five, and 847 ms for ten. Twenty and fifty questions exceeded this release's validated 2,048-token context; they are not advertised as supported.

Laya and Jev comparison rows above are figures published by the Laya benchmark page. They were not rerun in the xMESH environment.

Run on Apple silicon

Validated with Python 3.12 on an Apple M4 with 16 GB unified memory.

python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python examples/inference.py --request examples/request.json --device mps

Use --device cpu when MPS is unavailable. The request must contain state and questions; examples/request.json shows the typed contract.

Run with llama.cpp

llama-server \
  -m Singularity-xMESH-0.8B-BF16.gguf \
  --host 127.0.0.1 --port 8081 \
  -c 2048 -b 2048 -ub 2048 --parallel 1 -ngl 0

curl http://127.0.0.1:8081/v1/systemone \
  -H 'Content-Type: application/json' \
  --data-binary @examples/request.json

-ngl 0 is the portable CPU setting. On Apple silicon (Metal) or an NVIDIA GPU with the Vulkan build, -ngl 99 is verified to give the same decisions; see ACCELERATOR_PARITY.json.

Safety boundary

xMESH is an advisory router. It must not receive wallet secrets, construct signed payments, bypass approval, invent endpoints, or execute arbitrary URLs. Runtime code must restrict choices to retrieved candidate IDs and enforce budgets, networks, schemas, and approval rules independently.

Limitations

Specialized for x402 service routing; it is not a general chat model.
It does not retrieve services, verify providers, quote payments, or execute requests.
Service-selection accuracy is strong but not perfect.
Confidence is advisory and does not replace deterministic policy checks.
The validated context contract is 2,048 tokens.

License

The model and Singularity runtime files are Apache-2.0. Qwen and Cloudflare Clef attribution is included in THIRD_PARTY_NOTICES.md.

Downloads last month: 195

Safetensors

Model size

0.9B params

Tensor type

BF16

F32

Model tree for singularity-layer/Singularity-xMESH-0.8B

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(350)

this model

Quantizations

1 model

singularity-layer
/

Singularity-xMESH-0.8B

Singularity xMESH 0.8B

Intended behavior

Training data

Hardware compatibility

Portable GGUF runtime

Frozen x402 regression evaluation

General typed-decision evaluation

Run on Apple silicon

Run with llama.cpp

Safety boundary

Limitations

License

Model tree for singularity-layer/Singularity-xMESH-0.8B

Dataset used to train singularity-layer/Singularity-xMESH-0.8B