Instructions to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Carnice-MoE-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-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 samuelcardillo/Carnice-MoE-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use samuelcardillo/Carnice-MoE-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf samuelcardillo/Carnice-MoE-35B-A3B-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 "samuelcardillo/Carnice-MoE-35B-A3B-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"

Carnice-MoE-35B-A3B-GGUF / README.md

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metadata

language:
  - en
license: apache-2.0
tags:
  - qwen3.5
  - moe
  - hermes
  - agentic
  - tool-calling
  - qlora
  - unsloth
  - carnice
base_model: Qwen/Qwen3.5-35B-A3B
datasets:
  - bespokelabs/Bespoke-Stratos-17k
  - AI-MO/NuminaMath-CoT
  - kai-os/carnice-glm5-hermes-traces
  - open-thoughts/OpenThoughts-Agent-v1-SFT

Carnice MoE 35B-A3B — Hermes-Focused Agentic Model (GGUF)

QLoRA fine-tune of Qwen3.5-35B-A3B (MoE, 3B active parameters) optimized for agentic workflows and Hermes Agent runtime. Two-stage training adapted from kai-os/Carnice-9b.

Credits

Training methodology adapted from kai-os/Carnice-9b — same two-stage approach and datasets, applied to the larger MoE architecture. Key inspiration: training on actual Hermes Agent execution traces for native agentic behavior.

Available Quantizations

Quantization	Size	BPW	Min VRAM
Q8_0	35 GB	8.52	1x 48GB GPU
Q6_K	27 GB	6.58	1x 32GB GPU
Q5_K_M	24 GB	5.70	1x 32GB GPU
Q4_K_M	20 GB	4.87	1x 24GB GPU
MXFP4_MOE	19 GB	4.39	1x 24GB GPU

For BF16 safetensors, see samuelcardillo/Carnice-MoE-35B-A3B.

Model Details

Property	Value
Base Model	Qwen/Qwen3.5-35B-A3B
Architecture	Mixture of Experts (MoE)
Total Parameters	~35B
Active Parameters	~3B per token

What Makes This Different

Unlike generic reasoning distillation, this model was trained on actual Hermes Agent execution traces — real conversations where an AI agent:

Executes terminal commands and processes output
Performs file editing operations
Chains multi-step tool calls with results feeding back
Uses browser-assisted workflows
Makes decisions based on environmental feedback

This teaches the model the exact conversation patterns Hermes expects, rather than just generic reasoning.

Training Details

Two-Stage Approach

Stage A — Reasoning Repair (1 epoch)

Strengthens base model reasoning before agent-specific training
Loss: 0.4159

Dataset	Examples
bespokelabs/Bespoke-Stratos-17k	16,710
AI-MO/NuminaMath-CoT	17,000 (capped)

Stage B — Hermes Traces (2 epochs)

Agent-specific behavioral training on real execution traces
Loss: 0.3115

Dataset	Examples
kai-os/carnice-glm5-hermes-traces	1,627 (high quality)
open-thoughts/OpenThoughts-Agent-v1-SFT	15,209

Training Configuration

Parameter	Stage A	Stage B
LoRA Rank	64	64
LoRA Alpha	64	64
LoRA Targets	q, k, v, o projections	q, k, v, o projections
Learning Rate	2e-5 (linear)	1e-5 (cosine)
Epochs	1	2
Effective Batch	12	12
Context Length	4096	4096
Precision	4-bit QLoRA + BF16 adapters	Same
GPU	RTX PRO 6000 Blackwell (96GB)	Same
Total Training Time	~44 hours (both stages)

Trainable Parameters

6,881,280 (0.02% of 35B total)

Usage with llama.cpp

llama-server \
  --model Carnice-MoE-35B-A3B-Q8_0.gguf \
  --n-gpu-layers -1 \
  --ctx-size 131072 \
  --host 0.0.0.0 --port 8082

Acknowledgements

kai-os — Carnice training methodology and Hermes traces dataset
open-thoughts — Agent SFT dataset
bespokelabs — Bespoke-Stratos reasoning dataset
Unsloth — QLoRA training framework
Qwen — Base model