Inkling-Small-UD-Q4_K_XL-layers

Instructions to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers 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 lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
# Run inference directly in the terminal:
llama cli -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
# Run inference directly in the terminal:
llama cli -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers: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 lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
# Run inference directly in the terminal:
./llama-cli -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers: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 lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Use Docker

docker model run hf.co/lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

LM Studio
Jan

vLLM

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lyssquant/Inkling-Small-UD-Q4_K_XL-layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lyssquant/Inkling-Small-UD-Q4_K_XL-layers",
		"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/lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Ollama
How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Ollama:
```
ollama run hf.co/lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
```

Unsloth Studio

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lyssquant/Inkling-Small-UD-Q4_K_XL-layers to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lyssquant/Inkling-Small-UD-Q4_K_XL-layers to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lyssquant/Inkling-Small-UD-Q4_K_XL-layers to start chatting

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Docker Model Runner:
```
docker model run hf.co/lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16
```

Lemonade

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Run and chat with the model

lemonade run user.Inkling-Small-UD-Q4_K_XL-layers-BF16

List all available models

lemonade list

Hermes Agent

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers: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 lyssquant/Inkling-Small-UD-Q4_K_XL-layers:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lyssquant/Inkling-Small-UD-Q4_K_XL-layers with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lyssquant/Inkling-Small-UD-Q4_K_XL-layers: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 "lyssquant/Inkling-Small-UD-Q4_K_XL-layers: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"

lyssquant

jamesdumay commited on 11 days ago

Commit

2e22f64

0 Parent(s):

Duplicate from meshllm/Inkling-Small-UD-Q4_K_XL-layers

Browse files

Co-authored-by: James Dumay <jamesdumay@users.noreply.huggingface.co>

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projectors/mmproj-F32.gguf +3 -0
shared/embeddings.gguf +3 -0

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+---
+library_name: mesh-llm
+license: "apache-2.0"
+base_model:
+- "unsloth/Inkling-Small-GGUF"
+pipeline_tag: "image-text-to-text"
+tags:
+- gguf
+- mesh-llm
+- layer-package
+- skippy
+- distributed-inference
+- local-inference
+- openai-compatible
+---
+<div align="center">
+  <a href="https://www.meshllm.cloud">
+    <img src="https://meshllm.cloud/assets/images/jelly-logo-wordmark.png" alt="Mesh LLM" width="220">
+  </a>
+  <h1>Inkling-Small-UD-Q4_K_XL</h1>
+  <p>
+    <strong>Distributed GGUF inference package for Mesh LLM</strong>
+  </p>
+  <p>
+    <a href="https://www.meshllm.cloud"><img alt="Website" src="https://img.shields.io/badge/Website-meshllm.cloud-111111?style=for-the-badge"></a>
+    <a href="https://github.com/Mesh-LLM/mesh-llm"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Mesh--LLM-24292f?style=for-the-badge&logo=github"></a>
+    <a href="https://discord.gg/rs6fmc63eN"><img alt="Discord" src="https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge&logo=discord&logoColor=white"></a>
+  </p>
+</div>
+GGUF layer package for running **Inkling-Small-UD-Q4_K_XL** across a local Mesh LLM cluster.
+This package is derived from [unsloth/Inkling-Small-GGUF](https://huggingface.co/unsloth/Inkling-Small-GGUF) and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.
+## Highlights
+| Run locally | Pool multiple machines | OpenAI-compatible | Package variant |
+|---|---|---|---|
+| Private inference on your hardware | Split layers across peers | Serve `/v1/chat/completions` locally | `UD-Q4_K_XL` layer package |
+## Model Overview
+| Property | Value |
+|---|---|
+| **Source model** | [unsloth/Inkling-Small-GGUF](https://huggingface.co/unsloth/Inkling-Small-GGUF) |
+| **Model id** | `unsloth/Inkling-Small-GGUF:UD-Q4_K_XL` |
+| **Family** | Inkling |
+| **Parameter scale** | not recorded |
+| **Quantization** | `UD-Q4_K_XL` |
+| **Layer count** | 42 |
+| **Activation width** | 4096 |
+| **Package size** | 153.1 GB |
+| **Source file** | `UD-Q4_K_XL/Inkling-Small-UD-Q4_K_XL-00001-of-00005.gguf` |
+| **Package repo** | [meshllm/Inkling-Small-UD-Q4_K_XL-layers](https://huggingface.co/meshllm/Inkling-Small-UD-Q4_K_XL-layers) |
+| **License** | `apache-2.0` from [unsloth/Inkling-Small-GGUF](https://huggingface.co/unsloth/Inkling-Small-GGUF) |
+## Recommended Use
+- Local and private inference with Mesh LLM.
+- Multi-machine serving when the full GGUF is too large for one host.
+- OpenAI-compatible chat/completions workflows through Mesh LLM's local API.
+For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: [unsloth/Inkling-Small-GGUF](https://huggingface.co/unsloth/Inkling-Small-GGUF).
+## Quickstart
+```bash
+# Run this on each machine that should contribute memory/compute.
+mesh-llm serve --model "meshllm/Inkling-Small-UD-Q4_K_XL-layers" --split
+```
+```bash
+# Check the mesh and discover the OpenAI-compatible model name.
+curl -s http://localhost:3131/api/status
+curl -s http://localhost:3131/v1/models
+```
+```bash
+# Send an OpenAI-compatible chat request.
+curl -s http://localhost:3131/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "unsloth/Inkling-Small-GGUF:UD-Q4_K_XL",
+    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
+    "max_tokens": 128
+  }'
+```
+## Package Variant
+| Property | Value |
+|---|---|
+| **Format** | `layer-package` |
+| **Canonical source ref** | `unsloth/Inkling-Small-GGUF@1a19ef82883cb7b9c581b93c30ea252dabbf658d/UD-Q4_K_XL/Inkling-Small-UD-Q4_K_XL-00001-of-00005.gguf` |
+| **Source revision** | `1a19ef82883cb7b9c581b93c30ea252dabbf658d` |
+| **Source SHA-256** | `a51ac3f439198f2817219edd582be4b600c273be24e78cbd58ebff982d9f007e` |
+| **Skippy ABI** | `0.1.35` |
+| **Package manifest SHA-256** | `edae4bf5fd239d88fc2d20fb9eef31f6f7ce45ca743e1292827bc2a26e5f50c0` |
+## What Is Included
+| Artifact | Path | Contents | SHA-256 |
+|---|---|---|---|
+| Manifest | `model-package.json` | Package schema, source identity, checksums | `edae4bf5fd239d88fc2d20fb9eef31f6f7ce45ca743e1292827bc2a26e5f50c0` |
+| Metadata | `shared/metadata.gguf` | 1 tensors, 12.4 MB | `a30882bd63c7d3509acbe80f89fa3af26d2ea768a48d16aa29b39d0c31c57a65` |
+| Embeddings | `shared/embeddings.gguf` | 2 tensors, 846.7 MB | `5e25470fcee8342e220fcf4a9d580afe7b1bbcce3a6bf1173b528a7c20074a72` |
+| Output head | `shared/output.gguf` | 3 tensors, 846.7 MB | `66187e4186cab8645b1353feecdaaed350372202998a95ef5bbf860d38bd9a9d` |
+| Transformer layers | `layers/layer-*.gguf` | 42 layer artifacts, 998 tensors, 150.9 GB | `see model-package.json` |
+| Projector | `projectors/mmproj-BF16.gguf` | mmproj projector, 132.3 MB | `05d4475a956030be87b099865d6552a541a476db8cc3e266fcfa7c5a24846248` |
+| Projector | `projectors/mmproj-F16.gguf` | mmproj projector, 132.3 MB | `bf1e6afc9889151b3fee61119d3ee2cd224b59a1d881cdf87e33962f64aecc7e` |
+| Projector | `projectors/mmproj-F32.gguf` | mmproj projector, 264.5 MB | `653c88aab6699d65ecd45d82303a74a467309dcb8e837e0c9e18f394f4ee3def` |
+## Validation
+Generated by the Mesh LLM HF Jobs splitter from `mesh-llm` ref `main`.
+Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.
+```bash
+skippy-model-package write-package "/source/UD-Q4_K_XL/Inkling-Small-UD-Q4_K_XL-00001-of-00005.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_Inkling-Small-UD-Q4_K_XL-layers-193/package"
+```
+## Links
+- Source model: [unsloth/Inkling-Small-GGUF](https://huggingface.co/unsloth/Inkling-Small-GGUF)
+- Mesh LLM website: [meshllm.cloud](https://www.meshllm.cloud)
+- Mesh LLM: [github.com/Mesh-LLM/mesh-llm](https://github.com/Mesh-LLM/mesh-llm)
+- Discord: [discord.gg/rs6fmc63eN](https://discord.gg/rs6fmc63eN)
+- Package catalog: [meshllm/catalog](https://huggingface.co/datasets/meshllm/catalog)
+- Package format: [layer-package-repos.md](https://github.com/Mesh-LLM/mesh-llm/blob/main/docs/specs/layer-package-repos.md)

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+{
+  "schema_version": 1,
+  "queued_at": "2026-08-03T19:00:23.741267180+00:00",
+  "source_repo": "unsloth/Inkling-Small-GGUF",
+  "source_revision": "1a19ef82883cb7b9c581b93c30ea252dabbf658d",
+  "source_file": "UD-Q4_K_XL/Inkling-Small-UD-Q4_K_XL-00001-of-00005.gguf",
+  "source_projectors": [
+    {
+      "path": "mmproj-BF16.gguf",
+      "total_bytes": 138684128
+    },
+    {
+      "path": "mmproj-F16.gguf",
+      "total_bytes": 138684128
+    },
+    {
+      "path": "mmproj-F32.gguf",
+      "total_bytes": 277311968
+    }
+  ],
+  "quant": "UD-Q4_K_XL",
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