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.

[!NOTE] ~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
• NVFP4 (Spark / SGLang): https://huggingface.co/vcruz305/Muse-Glimmer-30B-Hermes-Agentic-NVFP4
• Serve recipes (SGLang + vLLM): https://github.com/vcruz305/muse-glimmer-nvfp4-spark-serve
• 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

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

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

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.

Model Card Authors

Victor Cruz.

Model Card Contact

This model page, or https://github.com/vcruz305/hermes-agentic-bench.

Acknowledgements

• Meta Superintelligence Lab — Muse Glimmer 30B (Apache 2.0)
• Unsloth — free Muse Conversational notebook and 4-bit pack
• Nous Research / Hermes Agent