Instructions to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Transformers:

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

pipe = pipeline("text-generation", model="Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5")
model = AutoModelForCausalLM.from_pretrained("Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 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 Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

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 Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

Use Docker

docker model run hf.co/Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

Llama new
LM Studio
Jan

vLLM

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

SGLang

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 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 "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5" \
    --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": "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5",
		"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 "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5" \
        --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": "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Ollama:
```
ollama run hf.co/Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16
```
Unsloth Desktop

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

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": "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16
```

Lemonade

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

Run and chat with the model

lemonade run user.Palette-RP-1.2B-Instruct-2609-v0.5-BF16

List all available models

lemonade list

Hermes Agent

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

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 Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16

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 "Indexnusrefather/Palette-RP-1.2B-Instruct-2609-v0.5:BF16" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Palette: New Series of Small Model Creative Distills With Only The Best Teachers!

Quick Overview: What is Palette-RP-1.2B-2609-Instruct-v0.5?

A lightweight experimental project performed on LFM2.5 1.2B Instruct, teached on Hy4 Preview RP chatlogs, nearly same methodology as Palette 4B.

Development for larger, higher quality dataset that will likely be based on the Hy4 as well is still in progress, it requiers significant investments and I plan to finish it in mid October if everything goes right.