Instructions to use DJLougen/hermes-qwen3.5-35b-a3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/hermes-qwen3.5-35b-a3b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/hermes-qwen3.5-35b-a3b-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": "DJLougen/hermes-qwen3.5-35b-a3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M

Ollama
How to use DJLougen/hermes-qwen3.5-35b-a3b-GGUF with Ollama:
```
ollama run hf.co/DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
```

Unsloth Studio

How to use DJLougen/hermes-qwen3.5-35b-a3b-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 DJLougen/hermes-qwen3.5-35b-a3b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DJLougen/hermes-qwen3.5-35b-a3b-GGUF to start chatting

How to use DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-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": "DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DJLougen/hermes-qwen3.5-35b-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 DJLougen/hermes-qwen3.5-35b-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 "DJLougen/hermes-qwen3.5-35b-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"

Docker Model Runner
How to use DJLougen/hermes-qwen3.5-35b-a3b-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/hermes-qwen3.5-35b-a3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/hermes-qwen3.5-35b-a3b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.hermes-qwen3.5-35b-a3b-GGUF-Q4_K_M

List all available models

lemonade list

Note: These models are optimized for use within an agentic harness (e.g. Hermes Agent) and may behave unexpectedly in raw inference without a system prompt. Capability benchmarks are strong but conversational behavior outside of a structured harness is not reliable. I am currently working on v2 to address this and reduce harness dependency.

Support This Work

I'm a PhD student who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. It's a hobby that got out of hand.

If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.

☕ ko-fi.com/djlougen

Hermes Qwen3.5 35B-A3B GGUF

GGUF quantizations of a Qwen3.5-35B-A3B model fine-tuned on NousResearch/hermes-function-calling-v1 for structured function calling and tool use.

Base Model

Architecture: Qwen3.5 MoE (Mixture of Experts) — 35B total parameters, ~3B active per token
Base: Qwen/Qwen3.5-35B-A3B
Context Length: 262,144 tokens
Experts: 256 total, 8 active per token

Fine-Tuning Details

Method: LoRA via Unsloth + TRL SFTTrainer
LoRA Rank (r): 32
LoRA Alpha: 32
LoRA Dropout: 0
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Precision: bf16
Optimizer: AdamW 8-bit
Learning Rate: 2e-4 with cosine scheduler
Warmup Steps: 10
Epochs: 3
Batch Size: 2 per device, 8 gradient accumulation steps (effective batch size 16)
Max Sequence Length: 4,096 tokens
Weight Decay: 0.01
PEFT Version: 0.18.1

Training Dataset

NousResearch/hermes-function-calling-v1 — a function-calling dataset following the Hermes Function-calling Standard. Includes:

Cleaned Glaive Function Calling samples
Advanced JSON structured output (agentic, multi-turn)
Single-turn JSON structured output samples

Conversations were formatted using ChatML (<|im_start|> / <|im_end|>) with role mapping: system, human -> user, gpt -> assistant, tool.

Quantization

All quantizations were produced using llama.cpp with an importance matrix (imatrix) computed from WikiText-2 calibration data for improved quality at lower bit depths.

Available Quants

Filename	Quant	Type	Size
hermes-qwen3.5-35b-a3b-f16.gguf	F16	Full precision	64.6 GB
hermes-qwen3.5-35b-a3b-Q8_0.gguf	Q8_0	Standard	36.9 GB
hermes-qwen3.5-35b-a3b-Q6_K.gguf	Q6_K	K-quant	28.5 GB
hermes-qwen3.5-35b-a3b-Q5_K_M.gguf	Q5_K_M	K-quant	24.7 GB
hermes-qwen3.5-35b-a3b-Q5_K_S.gguf	Q5_K_S	K-quant	24.0 GB
hermes-qwen3.5-35b-a3b-Q4_K_M.gguf	Q4_K_M	K-quant	21.2 GB
hermes-qwen3.5-35b-a3b-Q4_K_S.gguf	Q4_K_S	K-quant	19.9 GB
hermes-qwen3.5-35b-a3b-IQ4_NL.gguf	IQ4_NL	imatrix	19.8 GB
hermes-qwen3.5-35b-a3b-IQ4_XS.gguf	IQ4_XS	imatrix	18.7 GB
hermes-qwen3.5-35b-a3b-Q3_K_M.gguf	Q3_K_M	K-quant	16.8 GB
hermes-qwen3.5-35b-a3b-IQ3_M.gguf	IQ3_M	imatrix	15.4 GB
hermes-qwen3.5-35b-a3b-IQ3_S.gguf	IQ3_S	imatrix	15.3 GB
hermes-qwen3.5-35b-a3b-Q3_K_S.gguf	Q3_K_S	K-quant	15.2 GB
hermes-qwen3.5-35b-a3b-IQ3_XXS.gguf	IQ3_XXS	imatrix	13.6 GB
hermes-qwen3.5-35b-a3b-IQ2_M.gguf	IQ2_M	imatrix	11.7 GB
hermes-qwen3.5-35b-a3b-IQ2_S.gguf	IQ2_S	imatrix	10.7 GB
hermes-qwen3.5-35b-a3b-IQ2_XXS.gguf	IQ2_XXS	imatrix	9.5 GB
hermes-qwen3.5-35b-a3b-IQ1_M.gguf	IQ1_M	imatrix	8.2 GB
hermes-qwen3.5-35b-a3b-IQ1_S.gguf	IQ1_S	imatrix	7.5 GB

All quantizations verified: 733 tensors, GGUF v3.

Choosing a Quant

Q8_0 (36.9 GB): Closest to full precision. Use if you have the VRAM/RAM.
Q6_K / Q5_K_M (28.5 / 24.7 GB): Good balance of quality and size for most use cases.
Q4_K_M (21.2 GB): Popular sweet spot — significant size reduction with minimal quality loss.
IQ4_NL / IQ4_XS (19.8 / 18.7 GB): Importance-matrix 4-bit — can outperform standard Q4 quants at similar size.
IQ3_M / IQ3_S (15.4 / 15.3 GB): Importance-matrix 3-bit — good quality for the size with imatrix calibration.
IQ2_M and below (11.7 GB and smaller): Extreme compression with imatrix. Quality degrades progressively.
IQ1_M / IQ1_S (8.2 / 7.5 GB): Maximum compression. Expect significant quality loss.
IQ3_M and below: For constrained environments. Quality degrades progressively.
IQ2 / IQ1: Extreme compression. Expect notable quality degradation.

Usage

llama.cpp

llama-cli -m hermes-qwen3.5-35b-a3b-Q4_K_M.gguf -p "You are a helpful assistant." -cnv

LM Studio / Ollama / KoboldCpp

Download any GGUF file and load it directly.

Credits

Base Model: Qwen Team
Training Dataset: NousResearch
Fine-Tuning Framework: Unsloth
Quantization Tooling: llama.cpp

Downloads last month: 1,280

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for DJLougen/hermes-qwen3.5-35b-a3b-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Adapter

(30)

this model

DJLougen
/

hermes-qwen3.5-35b-a3b-GGUF

Support This Work

Hermes Qwen3.5 35B-A3B GGUF

Base Model

Fine-Tuning Details

Training Dataset

Quantization

Available Quants

Choosing a Quant

Usage

llama.cpp

LM Studio / Ollama / KoboldCpp

Credits

Model tree for DJLougen/hermes-qwen3.5-35b-a3b-GGUF

Dataset used to train DJLougen/hermes-qwen3.5-35b-a3b-GGUF