Instructions to use MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MainStack/marvy-2-35B-MoE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MainStack/marvy-2-35B-MoE-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": "MainStack/marvy-2-35B-MoE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M

Ollama
How to use MainStack/marvy-2-35B-MoE-GGUF with Ollama:
```
ollama run hf.co/MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
```

Unsloth Studio

How to use MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MainStack/marvy-2-35B-MoE-GGUF to start chatting

How to use MainStack/marvy-2-35B-MoE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MainStack/marvy-2-35B-MoE-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": "MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-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 MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use MainStack/marvy-2-35B-MoE-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MainStack/marvy-2-35B-MoE-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 "MainStack/marvy-2-35B-MoE-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"

Docker Model Runner
How to use MainStack/marvy-2-35B-MoE-GGUF with Docker Model Runner:
```
docker model run hf.co/MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
```

Lemonade

How to use MainStack/marvy-2-35B-MoE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.marvy-2-35B-MoE-GGUF-Q4_K_M

List all available models

lemonade list

marvy-2-35B-MoE-GGUF / README.md

tgetsov

Initial GGUF release: Q4_K_M + Q8_0

30f0dd3 verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

4.37 kB

	---
	license: apache-2.0
	base_model: stamsam/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-MLX-oQ4-MTP
	base_model_relation: quantized
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- servicenow
	- itsm
	- csdm
	- delivery
	- gguf
	- llama.cpp
	- ollama
	- lm-studio
	- quantized
	- qwen3.5
	- moe
	- mixture-of-experts
	---

	# marvy-2-35B-MoE-GGUF

	**GGUF quants of marvy-2, a 35B-A3B Mixture-of-Experts model fine-tuned for the
	ServiceNow delivery lifecycle.** ~3B active parameters per token; runs on a
	single consumer GPU or fast Apple Silicon thanks to the MoE sparsity.

	GGUF quantizations for use with [llama.cpp](https://github.com/ggml-org/llama.cpp),
	[Ollama](https://ollama.com), [LM Studio](https://lmstudio.ai), and compatible
	runtimes.

	> Released under Apache-2.0. Built with Qwen3.5 (Apache-2.0) via
	> `unsloth/Qwen3.6-35B-A3B` and the Opus-distilled `stamsam/...MTP` base.

	## Files

	\| File \| Quant \| Size \| Use when \|
	\|---\|---\|---\|---\|
	\| `marvy-2-35B-MoE-Q4_K_M.gguf` \| Q4_K_M \| ~20 GB \| Default — best size/quality balance \|
	\| `marvy-2-35B-MoE-Q8_0.gguf` \| Q8_0 \| ~34 GB \| Near-FP16 quality, more headroom \|

	## Architecture notes

	This is a hybrid SSM + MoE Transformer:

	- 40 layers, mixed SSM (Mamba-style) and grouped-query attention blocks
	- 256 routed experts per MoE layer, 3B active per token (A3B)
	- Shared expert per layer for common pathways
	- `qwen3_5_moe` architecture in llama.cpp; needs a recent build
	(see "Supported runtimes" below)

	The Multi-Token Prediction (MTP) head present in the base model was **not
	included** in this GGUF — these quants are text-only causal LM. The base
	model's MoE expert weights and SSM blocks are preserved.

	## Quick start

	### Ollama

	```bash
	ollama run hf.co/MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M
	```

	### llama.cpp

	```bash
	llama-cli -hf MainStack/marvy-2-35B-MoE-GGUF:Q4_K_M \
	-p "Write a ServiceNow user story with acceptance criteria for P1 SLA escalation." \
	--temp 0.4 \
	-c 4096
	```

	### LM Studio

	Search the model catalog for `marvy-2-35B-MoE-GGUF` and download, or load
	the local `.gguf` file via "Open Model in Folder". LM Studio's OpenAI-compatible
	server is at `http://localhost:1234/v1` by default.

	## Supported runtimes

	The `qwen3_5_moe` architecture (with mixed SSM layers and Multi-Token
	Prediction in the base) is new. Verify your runtime supports it:

	- llama.cpp: master branch as of mid-2026 (commits containing
	`Qwen3_5MoeForConditionalGeneration` registration in `conversion/qwen.py`).
	The Homebrew formula may lag — clone upstream if you hit
	"unknown architecture" errors.
	- Ollama: ships its own llama.cpp; check that your Ollama version is
	recent.
	- LM Studio: uses its own bundled runtime; recent versions support
	Qwen3.5 MoE.

	## Trained on

	- v1 corpus: ServiceNow delivery lifecycle artifacts (SOW, SDD, stories,
	acceptance criteria, value hypothesis, ...) — same data marvy-1 used.
	- v2 corpus: extended to capability-to-epic mapping, mermaid diagram
	authoring, deployment package modeling, stakeholder mapping, story-to-UAT,
	and more. See `EVAL.md` in the repo root for per-task perplexity.

	## How this GGUF was built

	```
	LoRA adapter (rank 32, 350 steps, attention-only Q/K/V/O)
	+
	bf16 base (unsloth/Qwen3.6-35B-A3B)
	│ mlx_lm fuse
	merged-bf16/ (14 shards, 65 GB safetensors with mlx_lm switch_mlp naming)
	│ scripts/marvy-v2-rename-moe-tensors.py (bridge to HF-canonical names)
	merged-bf16-hf/ (15 shards, 65 GB; switch_mlp → experts.gate_up_proj packed)
	│ llama.cpp/convert_hf_to_gguf.py --no-mtp --outtype f16
	marvy-2-35B-MoE-F16.gguf (65 GB, 733 tensors)
	│ llama-quantize
	marvy-2-35B-MoE-Q4_K_M.gguf (20 GB, 4.6 BPW)
	marvy-2-35B-MoE-Q8_0.gguf (34 GB, 8.52 BPW)
	```

	End-to-end build script: `scripts/marvy-v2-35B-MoE-build-gguf.sh` (in the
	source repo). The `switch_mlp → experts` rename is necessary because mlx_lm
	fuse and llama.cpp's converter use different naming conventions for routed
	MoE tensors.

	## License

	Apache-2.0 (inherits from Qwen). See `LICENSE` and `NOTICE` in the repo root.

	## Citation

	```bibtex
	@misc{marvy-2-35B-MoE,
	title = {marvy-2: A ServiceNow delivery-lifecycle MoE LLM},
	author = {MainStack},
	year = {2026},
	howpublished = {\url{https://huggingface.co/MainStack/marvy-2-35B-MoE-GGUF}}
	}
	```