Instructions to use appvoid/palmer-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-006 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-006")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-006")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-006", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use appvoid/palmer-006 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 appvoid/palmer-006
# Run inference directly in the terminal:
llama cli -hf appvoid/palmer-006

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/palmer-006
# Run inference directly in the terminal:
llama cli -hf appvoid/palmer-006

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 appvoid/palmer-006
# Run inference directly in the terminal:
./llama-cli -hf appvoid/palmer-006

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 appvoid/palmer-006
# Run inference directly in the terminal:
./build/bin/llama-cli -hf appvoid/palmer-006

Use Docker

docker model run hf.co/appvoid/palmer-006

LM Studio
Jan

vLLM

How to use appvoid/palmer-006 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-006"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-006",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/palmer-006

SGLang

How to use appvoid/palmer-006 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 "appvoid/palmer-006" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-006",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "appvoid/palmer-006" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-006",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use appvoid/palmer-006 with Ollama:
```
ollama run hf.co/appvoid/palmer-006
```

Unsloth Studio

How to use appvoid/palmer-006 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 appvoid/palmer-006 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 appvoid/palmer-006 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for appvoid/palmer-006 to start chatting

Atomic Chat new
Docker Model Runner
How to use appvoid/palmer-006 with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-006
```

Lemonade

How to use appvoid/palmer-006 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/palmer-006

Run and chat with the model

lemonade run user.palmer-006-{{QUANT_TAG}}

List all available models

lemonade list

Author's Message

pinned

by appvoid - opened 5 days ago

Discussion

appvoid

Owner 5 days ago

•

edited 3 days ago

The era of scaling alone is ending.

For years, bigger models meant better models. That is changing.

Today, a single person with enough dedication can build systems that were once only possible inside the largest AI labs. Progress is no longer defined by compute alone, it is defined by engineering, persistence, and ideas.

This release is the beginning of that shift.

We believe useful intelligence can be distilled into sub-billion parameter models. Our goal isn't to build the biggest models. It's to build the smartest models for their size.

Others will surpass this model in months, perhaps weeks. That's not the point.

The point is to prove that another future is possible, one where capable AI runs everywhere, belongs to everyone, and no longer depends on massive infrastructure.

This is just a spark.

Welcome to the next era.

appvoid pinned discussion 5 days ago

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