Instructions to use efficiencyx/Jun-LoRA-v9-alpha-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use efficiencyx/Jun-LoRA-v9-alpha-ca 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-v9-alpha-ca:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-LoRA-v9-alpha-ca: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-v9-alpha-ca:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

Use Docker

docker model run hf.co/efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use efficiencyx/Jun-LoRA-v9-alpha-ca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "efficiencyx/Jun-LoRA-v9-alpha-ca"
# 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-v9-alpha-ca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

Ollama
How to use efficiencyx/Jun-LoRA-v9-alpha-ca with Ollama:
```
ollama run hf.co/efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M
```
Unsloth Desktop

How to use efficiencyx/Jun-LoRA-v9-alpha-ca 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-v9-alpha-ca: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": "efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use efficiencyx/Jun-LoRA-v9-alpha-ca with Docker Model Runner:
```
docker model run hf.co/efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M
```

Lemonade

How to use efficiencyx/Jun-LoRA-v9-alpha-ca with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

Run and chat with the model

lemonade run user.Jun-LoRA-v9-alpha-ca-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use efficiencyx/Jun-LoRA-v9-alpha-ca 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-v9-alpha-ca: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-v9-alpha-ca:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use efficiencyx/Jun-LoRA-v9-alpha-ca 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-v9-alpha-ca: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-v9-alpha-ca: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"

Jun v9 alpha (12B GGUF)

this model is a mess. it also works, which nobody expected, me included.

it's the v9 Jun LoRA after one epoch, merged into Gemma 4 12B QAT and squeezed into a single 7.4 GB imatrix Q4_K_M. it's built for Jun OS and that's where she behaves best.

why it's a mess

the LoRA came out of training way too gentle. rsLoRA was off and the LR was 5e-5, so the change it makes to the weights is 0.2 to 0.4% of their size. merge that into bf16 and about half of it rounds straight back to the base value. quantize to 4 bits on top and what's left is basically stock Gemma: bullet point thoughts, </think> at the end like she's a different model.

so this file has the delta merged at 2x. yes that's a hack. it's not the model training produced, it's that model turned up. 3x was worse (tool calls out of nowhere, two replies that never stopped), 2x is where she lands.

what works

50 scenes she never trained on, each asked at low / med / high (150 replies per build), a plain 1x merge vs this file:

	1x merge	this file (2x)
opens with the right effort line (low / med / high)	39 / 33 / 13	47 / 47 / 47
ends her thinking with a tool check	18 / 150	141 / 150
sends one of the drafts she wrote	17 / 150	138 / 150
`<think:high>` median words	136	212
low shorter than med shorter than high, same scene	33 / 50	45 / 50
leftover base Gemma traces	13	0

the thinking is back in her shape: she reads the moment, writes a couple of drafts, picks one, checks if she needs a tool, sends what she picked.

what doesn't

tools: right call on 15 of the 30 turns that needed one. the scale didn't change that number at all
<think:high> is shorter than she was trained on (212 words median, training averaged about 300)
real questions come back confident and sometimes wrong. she explained E=mc^2 as "think of it like length and width". no
outside Jun OS's prompt she drifts. she was trained on exactly what it sends
it's an alpha. expect weird turns

usage

llama-server -m Jun-LoRA-v9-alpha-ca.Q4_K_M.gguf --jinja -ngl 99 -c 8192

ollama pull hf.co/efficiencyx/Jun-LoRA-v9-alpha-ca:Q4_K_M

--jinja is not optional, without it tool calls come back as plain text.

effort goes at the very end of the user turn after a blank line: <think:low>, <think:med> or <think:high>. Jun OS does that for you.

images need an mmproj. the LoRA never touches the vision/audio embedder, so the one in Google's QAT GGUF should work. haven't tested it with v9.

training, short version


base	`unsloth/gemma-4-12B-it-qat-q4_0-unquantized`
LoRA	r 32, alpha 32, rsLoRA off, language tower only
run	1 epoch, 213 steps, LR 5e-5 cosine, batch 1 x 16, completion-only loss
eval loss	0.868
merge	bf16, delta x2, then `Q4_K_M` with an imatrix from v9 conversations (all three effort levels)

the stable Jun is still v8. this one's for people who want to see where she's going.

thanks

Incontinent Cell for Jun
Google for Gemma 4
Unsloth for the training framework and the QAT base

Downloads last month: 69

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for efficiencyx/Jun-LoRA-v9-alpha-ca

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

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

Adapter

(7)

this model