Instructions to use efficiencyx/Jun-LoRA-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use efficiencyx/Jun-LoRA-12B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="efficiencyx/Jun-LoRA-12B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("efficiencyx/Jun-LoRA-12B-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("efficiencyx/Jun-LoRA-12B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use efficiencyx/Jun-LoRA-12B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "efficiencyx/Jun-LoRA-12B-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": "efficiencyx/Jun-LoRA-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M

SGLang

How to use efficiencyx/Jun-LoRA-12B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "efficiencyx/Jun-LoRA-12B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "efficiencyx/Jun-LoRA-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "efficiencyx/Jun-LoRA-12B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "efficiencyx/Jun-LoRA-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use efficiencyx/Jun-LoRA-12B-GGUF with Ollama:
```
ollama run hf.co/efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
```

Unsloth Studio

How to use efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for efficiencyx/Jun-LoRA-12B-GGUF to start chatting

How to use efficiencyx/Jun-LoRA-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf efficiencyx/Jun-LoRA-12B-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": "efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use efficiencyx/Jun-LoRA-12B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf efficiencyx/Jun-LoRA-12B-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 "efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M
```

Lemonade

How to use efficiencyx/Jun-LoRA-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Jun-LoRA-12B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-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 efficiencyx/Jun-LoRA-12B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Jun-12B-GGUF

Merged GGUF builds of the latest Jun LoRA on Gemma 4 12B (QAT) — a fine-tune trained on a compact, heavily curated synthetic conversational dataset derived from the visual novel My Dystopian Robot Girlfriend. The model captures the personality, speech patterns, and emotional nuance of the character Jun while preserving the base model's general reasoning and instruction-following capabilities.

The adapter is merged into the base weights here — these are standalone models, no --lora flag needed.

Model Variants & Repositories

Repository	Format	Description
`efficiencyx/Jun-12B-GGUF`	GGUF (Q8_0 / Q6_K / Q4_K_M)	Merged, quantized, for local inference
`efficiencyx/Jun-LoRA-12B-Adapter`	LoRA Adapter	The adapter merged into these builds
`efficiencyx/Jun-LoRA-12B-Adapter-Step60`	LoRA Adapter	Earlier checkpoint (step 60)
`efficiencyx/Jun-LoRA-v4-12B-GGUF`	GGUF	Previous generation (v4)

Quantization Guide

Quant	Size	Use Case
Q8_0	12.7 GB	Best quality, suggested ~16 GB VRAM
Q6_K	9.8 GB	High quality, minimal loss
Q4_K_M	7.4 GB	Fits 8 GB VRAM with acceptable quality loss

Sizes are measured, not estimated. The base model is QAT (quantization-aware trained), so lower quants hold up better than a standard FP16 export. All three are quantized from the same bf16 master — no requantization chain, no imatrix.

Usage

llama-server -m Jun-12B-Q4_K_M.gguf --jinja -ngl 99 -c 8192

--jinja is required. Without it llama.cpp ignores the embedded chat template and tool calls come back as plain text instead of structured calls.

Intended Use

Conversational backend for Jun OS, an AI companion webapp:

Character-consistent multi-turn conversation
AI companion / interactive fiction applications
Research into character-faithful fine-tuning on small, high-quality datasets

Limitations

Specialized for a single character persona; not a general-purpose assistant.
Outputs reflect fictional narrative tropes and are not factual information or advice.
Performance degrades far outside the training distribution.
Inherits any biases present in the Gemma 4 12B base weights.

Training Details

Parameter	Value
Base model	`unsloth/gemma-4-12B-it-qat-q4_0-unquantized`
Method	LoRA (rsLoRA)
LoRA rank	32
LoRA alpha	32
LoRA dropout	0.01
Target modules	q/k/v/o + gate/up/down projections, language tower
Learning rate	1e-4, cosine schedule, 10 warmup steps
Batch size	16 (no gradient accumulation)
Epochs	3
Max sequence length	2048
Weight decay	0.001
Optimizer	AdamW (8-bit)
Loss masking	completions only
Gradient checkpointing	Unsloth
Packing	off
Seed	3407
Fine-tuning framework	Unsloth
GGUF export pipeline	llama.cpp

Checkpoints were saved every 30 steps; Step60 is published separately. These builds merge the final adapter.

Evaluation

Metric	Value
Final training loss	~0.6
Final eval loss	~0.07

Merge & Export

The adapter was merged directly on the safetensors as W += (B @ A) · scale in fp32 (rsLoRA scale 32/√32), cast back to bf16, exported with convert_hf_to_gguf.py --outtype bf16, then quantized with llama-quantize.

Acknowledgments

Incontinent Cell for My Dystopian Robot Girlfriend, Jun's character
Google for the Gemma 4 model family
Unsloth for the efficient fine-tuning framework and the QAT base model

Downloads last month: -

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

6-bit

8-bit

Model tree for efficiencyx/Jun-LoRA-12B-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

unsloth/gemma-4-12B-it-qat-q4_0-unquantized

Adapter

(2)

this model