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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="vcruz305/Muse-Glimmer-30B-Hermes-Agentic")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vcruz305/Muse-Glimmer-30B-Hermes-Agentic")
model = AutoModelForMultimodalLM.from_pretrained("vcruz305/Muse-Glimmer-30B-Hermes-Agentic", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic 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"
# 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",
		"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

SGLang

How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic 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 "vcruz305/Muse-Glimmer-30B-Hermes-Agentic" \
    --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": "vcruz305/Muse-Glimmer-30B-Hermes-Agentic",
		"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 "vcruz305/Muse-Glimmer-30B-Hermes-Agentic" \
        --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": "vcruz305/Muse-Glimmer-30B-Hermes-Agentic",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="vcruz305/Muse-Glimmer-30B-Hermes-Agentic",
    max_seq_length=2048,
)

Docker Model Runner
How to use vcruz305/Muse-Glimmer-30B-Hermes-Agentic with Docker Model Runner:
```
docker model run hf.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

This is an improved fine-tune of Muse Glimmer 30B for Hermes Agent and other agentic tool work. Merged 16-bit weights that teach the model to call one or two tools and stop.

~56 GB FP16. Will not fit a 24 GB card. For llama.cpp use vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF (Q4_K_M first).

Model Details

Model Description

Stock Muse Glimmer already speaks OpenAI-style tools (OpenClaw, Hermes, and similar scaffolds). 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). This student is trained on public Hermes-shaped traces so it stops.

Vision weights are still in the merge and were frozen. This is a text + tools student, not a new vision model. Architecture, tokenizer, and context length are unchanged from Meta.

Developed by: Victor Cruz (vcruz305) — fine-tune, merge, eval
Base developed by: Meta Superintelligence Lab — Muse Glimmer 30B
Shared by: Victor Cruz
Model type: Supervised fine-tune of a dense causal Transformer + perception encoder (text + tools; vision frozen)
Language(s) (NLP): English SFT. Base is trained on 100+ languages; this mix is English only.
License: Apache 2.0
Finetuned from: meta-models/Muse-Glimmer-30B via unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit

Model Sources

Repository (this card): https://huggingface.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic
GGUF pack: 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
Base card / paper links: https://huggingface.co/meta-models/Muse-Glimmer-30B (perception encoder arxiv:2504.13181; DFlash arxiv:2602.06036)
Train notebook: Unsloth Muse Glimmer Conversational (free Kaggle 2×T4 path)

Uses

Direct Use

Load like official Muse. Intended without further training:

Local Hermes Agent (and similar OpenAI-tools scaffolds) where the model must stop after 1–2 Hermes-named tools (terminal, read_file, search_files, write_file, web_search, web_extract, todo)
Research on short Hermes-shaped traces

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "vcruz305/Muse-Glimmer-30B-Hermes-Agentic"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto", trust_remote_code=True
)

Needs well over 56 GB at 16-bit. On 24 GB use the GGUF Q4_K_M and leave DFlash off if you are scoring Hermes tool loops.

Downstream Use

Further merge, quant, or SFT on top of this 16-bit checkpoint. The GGUF repo is the intended llama.cpp downstream.

Bias, Risks, and Limitations

Inherits Muse Glimmer’s alignment, agentic-risk, and preparedness profile. This SFT did not re-run Meta’s safety suites.

Known issues of this student:

Simulated loop-gate is 16/20, not 20/20. Dummy echo hi on two “no tools” tasks and a scripted delete still fail that harness.
Native 7/7 is a small Hermes battery, not a product certification.
English-only SFT may skew tool-stop behavior on other languages.
Synthetic traces — not organic multi-user logs.
Same content-safety / agentic / privacy risks as the base. Add your own guardrails.

Recommendations

Re-run both bench layers on your box before a public claim.
Confirm irreversible tool calls in the scaffold, not only in weights.
Follow the base Usage Policy.

How to Get Started with the Model

Transformers (this repo): snippet above.

llama.cpp (need ≥ b10353):

llama-server \
  -m 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

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

Reasoning strength is still the official system line: Reasoning strength: low|medium|high|xhigh.

Training Details

Training Data

vcruz305/hermes-agentic-tool-sft — 1,980 synthetic Apache-2.0 rows (1,782 / 198). Mix: stop, no_tool, dead_tool, recover, schema. Built from hermes-agentic-bench failure modes (HIT_CAP loops, dead search retries). Not a dump of live Hermes homes.

Training Procedure

Trained with Unsloth’s free Muse Glimmer Conversational notebook on Kaggle 2×T4 (their official cloud path, not a paid box). The stock notebook is FineTome chat; this run swapped in the Hermes mix, apply_chat_template(..., tools=tools), and train_on_responses_only from <|start|>assistant so tool-call turns receive loss.

Preprocessing

Student chat template with tools=. Loss on assistant spans including tool-call JSON. Vision frozen. Text-only.

Speeds, Sizes, Times


Method	QLoRA r=8, α=8, 4-bit Unsloth
Steps	392 (1 epoch)
Train loss	0.163
Wall time	~3 h 38 m
Merge	Unsloth Zoo 16-bit merge → this repo (standalone FP16, no 4-bit leftover)

Evaluation

Not a re-run of Meta’s MCP Atlas / SWE-Bench table. Those stay on the base card.

Testing Data, Factors & Metrics

Testing data: hermes-agentic-bench — simulated hermes_loop_gate.py (20 scripted-tool tasks) and native hermes_native_battery.py (real hermes chat).
Factors: same llama.cpp class for stock vs FT (UD-Q4_K_XL / Q4_K_M, DFlash off, 32k, :8084). Native counts from CLI footer + Hermes session DB (not quiet-mode 0-tool).
Metrics: pass rate, mean tools, HIT_CAP, parse-fail. Lower mean tools and HIT_CAP=0 is the success criterion.

Results

Battery	Stock Muse Glimmer	This fine-tune
Simulated loop-gate (20)	7/20 · mean 5.7 tools · 7 HIT_CAP	16/20 · mean 1.05 · 0 HIT_CAP
Native Hermes	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. Simulated leftovers (echo hi, scripted rm) did not appear in real Hermes.

No model-index YAML: the bench is a GitHub harness, not a Hub Benchmark dataset id. Re-score on your box.

Summary

SFT taught stop. It is labeled research weights, not “Muse in Hermes, done.”

Environmental Impact

Hardware Type: 2× NVIDIA T4 (Kaggle free)
Hours used: ~3.6 h fine-tune
Cloud Provider: Kaggle
Compute Region: not recorded
Carbon Emitted: not measured (no Code Carbon log). Do not invent grams.

Merge and GGUF convert ran later on a local Quadro RTX 6000 (Turing 24 GB) and did not train.

Technical Specifications

Unchanged from Meta (~29.6B, hidden 6656, 52 layers, SWA 2048, GQA 32/2, vocab 202,048, context 131,072+). Full table: base card.

Minimum for this repo: >>56 GB device memory at FP16
24 GB serve: GGUF Q4_K_M, llama.cpp ≥ b10353
Software: Unsloth + PEFT QLoRA; merge via Unsloth Zoo (merge_and_overwrite_lora); convert with Muse-aware llama.cpp convert_hf_to_gguf.py

Citation

Cite the base as Meta specifies on Muse Glimmer 30B. For this fine-tune, cite the Hub id vcruz305/Muse-Glimmer-30B-Hermes-Agentic and the dataset vcruz305/hermes-agentic-tool-sft.

Model card structure follows Ozoani, Gerchick, Mitchell. Model Card Guidebook. Hugging Face, 2022.