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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

File size: 3,064 Bytes

39db083

{
  "schema_version": 1,
  "source": {
    "format": "merged BF16 safetensors",
    "model_sha256": "2dda751389cf7d98cfed72a16e6d8ce253dcd125b84870713cd33d1637e0ab84",
    "decision_head_sha256": "ff6433e096130f286038a1bfff07741018d6c8f6ccb91ee325419de717e87ff4"
  },
  "conversion": {
    "llama_cpp_tag": "b11719",
    "llama_cpp_commit": "5aeac37a35b44fde4bdffaec9159f2fcb253b622",
    "bf16_gguf_sha256": "9f4c08f19c4d203059bc74ed2754e800ec472d7276d8ddb2321480e86d392fff"
  },
  "release_artifact": {
    "path": "Singularity-xMESH-0.8B-BF16.gguf",
    "format": "GGUF BF16",
    "bytes": 1571529600,
    "sha256": "9f4c08f19c4d203059bc74ed2754e800ec472d7276d8ddb2321480e86d392fff",
    "context_tokens": 2048,
    "physical_batch_tokens": 2048,
    "node_default_slots": 1
  },
  "accelerated_artifact": {
    "path": "Singularity-xMESH-0.8B-Q8_0.gguf",
    "format": "GGUF Q8_0",
    "bytes": 841069440,
    "sha256": "41b36270b67f6c595e98cf4f29d6a0d13f0c19daa37b75741cd8296e2729fd75",
    "cpu_status": "rejected for universal use: llama.cpp b11719 exits 139 during quantized Clef CPU initialization"
  },
  "q8_metal_frozen_x402_export_parity": {
    "cases": 80,
    "decisions": 960,
    "exact_cases": 80,
    "matching_decisions": 960,
    "median_request_ms": 1542.8355415,
    "p95_request_ms": 4466.246708,
    "runtime": "llama.cpp b11719, Apple M4 Metal, two validation slots"
  },
  "bf16_cpu_frozen_x402_export_parity": {
    "cases": 80,
    "decisions": 960,
    "exact_cases": 80,
    "matching_decisions": 960,
    "median_request_ms": 3109.9937505,
    "p95_request_ms": 3495.010333,
    "runtime": "llama.cpp b11719, Apple M4 CPU, one release-profile slot"
  },
  "rejected_artifacts": [
    {
      "format": "GGUF Q4_K_M",
      "bytes": 545162112,
      "sha256": "0f13d08daa156d8f9cec6aa9a2488861ac0a0e1749a7ba04cfccec9662c1c302",
      "reason": "Changed 16 of 960 decisions and preserved only 65 of 80 complete flows."
    }
  ],
  "typed_decisions": {
    "artifact": "Singularity-xMESH-0.8B-Q8_0.gguf",
    "dataset": "LocalLLaMA/typed-decisions",
    "revision": "7189f46b02248bd2569df7325c9df8e3b24e90a7",
    "split": "all/test",
    "cases": 400,
    "decisions": 2000,
    "accuracy": 0.4775,
    "row_exact_match": 0.045,
    "soft_accuracy": 0.4151943743,
    "brier_vs_soft": 0.40858675,
    "ece": 0.2928255711,
    "score_mae": 0.6424282911,
    "score_within_one": 0.765,
    "median_case_ms": 1201.44,
    "p95_case_ms": 1353.346917
  },
  "option_order": {
    "seed": 13,
    "choice_questions": 600,
    "answer_flips": 0,
    "mean_probability_total_variation": 0.0,
    "max_probability_total_variation": 0.0
  },
  "question_scaling": {
    "runtime": "llama.cpp b11719, Apple M4 Metal, sequential caller",
    "one_question_p50_ms": 167.6057495,
    "five_questions_p50_ms": 448.471604,
    "ten_questions_p50_ms": 846.7270625,
    "twenty_questions": "unsupported: 2070 input tokens exceeds the 2048-token contract",
    "fifty_questions": "unsupported: 5070 input tokens exceeds the 2048-token contract"
  }
}