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"

samuelcardillo commited on Apr 9

Commit

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1 Parent(s): 188256d

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README.md +118 -0

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	@@ -0,0 +1,118 @@

+---
+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](https://huggingface.co/kai-os/Carnice-9b).
+## Credits
+Training methodology adapted from **[kai-os/Carnice-9b](https://huggingface.co/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](https://huggingface.co/samuelcardillo/Carnice-MoE-35B-A3B).
+## Model Details
+| Property | Value |
+|---|---|
+| Base Model | [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/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](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 16,710 |
+| [AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/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](https://huggingface.co/datasets/kai-os/carnice-glm5-hermes-traces) | 1,627 (high quality) |
+| [open-thoughts/OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/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
+```bash
+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](https://huggingface.co/kai-os)** — Carnice training methodology and Hermes traces dataset
+- **[open-thoughts](https://huggingface.co/open-thoughts)** — Agent SFT dataset
+- **[bespokelabs](https://huggingface.co/bespokelabs)** — Bespoke-Stratos reasoning dataset
+- **[Unsloth](https://unsloth.ai)** — QLoRA training framework
+- **[Qwen](https://huggingface.co/Qwen)** — Base model