Instructions to use efficiencyx/Jun-Lora-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use efficiencyx/Jun-Lora-v2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="efficiencyx/Jun-Lora-v2-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-v2-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("efficiencyx/Jun-Lora-v2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use efficiencyx/Jun-Lora-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-Lora-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf efficiencyx/Jun-Lora-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf efficiencyx/Jun-Lora-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use efficiencyx/Jun-Lora-v2-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-v2-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-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M

SGLang

How to use efficiencyx/Jun-Lora-v2-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-v2-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-v2-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-v2-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-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use efficiencyx/Jun-Lora-v2-GGUF with Ollama:
```
ollama run hf.co/efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use efficiencyx/Jun-Lora-v2-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-v2-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": "efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use efficiencyx/Jun-Lora-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M
```

Lemonade

How to use efficiencyx/Jun-Lora-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull efficiencyx/Jun-Lora-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Jun-Lora-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use efficiencyx/Jun-Lora-v2-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-v2-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-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use efficiencyx/Jun-Lora-v2-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-v2-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-v2-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"

efficiencyx commited on Jun 22

Commit

bf4fdf9

verified ·

1 Parent(s): e28399c

Trained with Unsloth - config

Browse files

Files changed (1) hide show

config.json +170 -0

config.json ADDED Viewed

	@@ -0,0 +1,170 @@

+{
+  "architectures": [
+    "Gemma4UnifiedForConditionalGeneration"
+  ],
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+    "label2id": {
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+    "return_dict": true,
+    "rms_norm_eps": 1e-06
+  },
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+    "attention_dropout": 0.0,
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+        "rope_type": "proportional"
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+}