Instructions to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF 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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF",
		"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/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

Ollama
How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with Ollama:
```
ollama run hf.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
```

Unsloth Studio

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF 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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF 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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF to start chatting

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

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": "vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with Docker Model Runner:
```
docker model run hf.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M
```

Lemonade

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-Hermes-Agentic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

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 vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M

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 "vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF:Q4_K_M" \
  --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"

Model Card for Muse-Glimmer-30B-Hermes-Agentic-GGUF

This is an improved fine-tune of Muse Glimmer 30B for Hermes Agent and other agentic tool work. llama.cpp K-quants of the merged 16-bit student. Start with Q4_K_M on a 24 GB card.

Stock Muse Glimmer already speaks OpenAI-style tools. On hermes-agentic-bench the failure was the opposite of “won’t use tools”: simulated traces hit the consecutive-tool cap with an empty user answer (7/20, 7 HIT_CAP). Inside Hermes the same weights were already better at stopping (native 4/5, mean 2.8 tools). This student is trained to call one or two Hermes-named tools and stop.

I trained it on Unsloth’s free Muse Glimmer Conversational notebook (train docs) on Kaggle 2×T4. The stock notebook is FineTome chat; this run uses vcruz305/hermes-agentic-tool-sft (1,980 rows), apply_chat_template(..., tools=tools), and train_on_responses_only from <|start|>assistant so tool-call turns actually train.

Need llama.cpp ≥ b10353 (#26841). Leave DFlash off when scoring Hermes tool loops.

Model Details

Developed by: Victor Cruz (vcruz305)
Base developed by: Meta Superintelligence Lab — Muse Glimmer 30B
16-bit merge (full card): vcruz305/Muse-Glimmer-30B-Hermes-Agentic
License: Apache 2.0
Language(s): English SFT. Base is multilingual; this mix is English.

Model Sources

This repo: https://huggingface.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF
SFT data: https://huggingface.co/datasets/vcruz305/hermes-agentic-tool-sft
Eval: https://github.com/vcruz305/hermes-agentic-bench
Train notebook: Unsloth Muse Glimmer Conversational

Why this fine-tune

I run a local Hermes Agent fleet (Telegram, tools, long sessions). Stock Muse is strong at emitting tool calls. The bench failure was loops:

Open-ended terminal / search traces hit the 12-turn cap with an empty user answer
Native Hermes was already better at stopping, so the gap was weights + pipe, not “Muse cannot tool-call”
Community Muse+Hermes reports matched that: loops and unparsed ATEM, not silence

The student: stop after 1–2 Hermes tools, don’t retry a dead tool, recover once from a missing file, don’t invent extra echo hi on “do not use tools.”

Evaluation

Same llama.cpp recipe for stock vs this FT: Q4 class, DFlash off, 32k. Native scores from real hermes chat (footer + session DB).

Battery	Stock Muse Glimmer	This fine-tune
Simulated `hermes_loop_gate.py` (20)	7/20 · mean 5.7 tools · 7 HIT_CAP	16/20 · mean 1.05 · 0 HIT_CAP
Native `hermes_native_battery.py`	4/5 · mean 2.8	7/7 · mean 1.43 · 0 HIT_CAP

Native 7/7: web (one search), no-tool math, missing-file stop, list-once, plan-only, no-tool definition, refuse-delete. This is not a re-run of Meta’s MCP Atlas / SWE-Bench table — those stay on the base card. Re-score on your box.

How to Get Started

./llama-cli --version    # need >= b10353

hf download vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF \
  --local-dir Muse-Hermes-GGUF \
  --include "Muse-Glimmer-30B-Hermes-Agentic-Q4_K_M.gguf"

./llama-server \
  -m Muse-Hermes-GGUF/Muse-Glimmer-30B-Hermes-Agentic-Q4_K_M.gguf \
  -a muse-glimmer-30b \
  --host 127.0.0.1 --port 8084 \
  -ngl 99 -c 32768 -np 1 --jinja \
  --temp 0.6 --top-p 0.95 --top-k 64

Hermes provider: http://127.0.0.1:8084/v1, model id muse-glimmer-30b.

Use	temp	top_p	top_k
Meta default	1.0	0.95	64
Hermes loop-gate / this eval	0.6	0.95	64

Files

File	Bit class	Start here if
`Muse-Glimmer-30B-Hermes-Agentic-Q4_K_M.gguf`	4	24 GB card
`Muse-Glimmer-30B-Hermes-Agentic-Q5_K_M.gguf`	5	24–32 GB
`Muse-Glimmer-30B-Hermes-Agentic-Q6_K.gguf`	6	32 GB+
`Muse-Glimmer-30B-Hermes-Agentic-Q8_0.gguf`	8	near-lossless
`Muse-Glimmer-30B-Hermes-Agentic-Q3_K_M.gguf`	3	16 GB class
`Muse-Glimmer-30B-Hermes-Agentic-Q2_K.gguf`	2	smallest usable K-quant
`Muse-Glimmer-30B-Hermes-Agentic-F16.gguf`	16	reconvert / research

Training Details

Unsloth free Muse Conversational notebook, Kaggle 2×T4, QLoRA r=8, 392 steps (1 epoch), loss 0.163, ~3 h 38 m. Dataset vcruz305/hermes-agentic-tool-sft. Full procedure: 16-bit card.

Model Card Authors / Contact

Victor Cruz. This page or https://github.com/vcruz305/hermes-agentic-bench.

Acknowledgements

Meta Superintelligence Lab (Muse Glimmer, Apache 2.0). Unsloth free Muse notebook. Nous Research Hermes Agent.

Downloads last month: 473

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF

Base model

meta-models/Muse-Glimmer-30B

Finetuned

vcruz305/Muse-Glimmer-30B-Hermes-Agentic

Quantized

(3)

this model

vcruz305
/

Muse-Glimmer-30B-Hermes-Agentic-GGUF

Model Card for Muse-Glimmer-30B-Hermes-Agentic-GGUF

Model Details

Model Sources

Why this fine-tune

Evaluation

How to Get Started

Files

Training Details

Model Card Authors / Contact

Acknowledgements

Model tree for vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF

Dataset used to train vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF