Text Generation
Transformers
Safetensors
GGUF
English
falcon_h1
palmer
text-editing
rewriting
paraphrasing
grammar-correction
edge
small-language-model
Instructions to use appvoid/palmer-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
- Atomic Chat
| #!/usr/bin/env python3 | |
| # quick-start for completion models: | |
| # 1. start a server: ``llama-server -m model.gguf`` | |
| # 2. create a text file with your prompt like `prompt.txt` | |
| # 3. run a completion: ``python completion.py prompt.txt`` | |
| import argparse | |
| import sys | |
| from pathlib import Path | |
| import requests | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser( | |
| description="Send a text file as a raw prompt to llama.cpp server." | |
| ) | |
| parser.add_argument( | |
| "prompt_file", | |
| type=Path, | |
| help="UTF-8 text file containing the raw prompt", | |
| ) | |
| parser.add_argument( | |
| "-o", | |
| "--output", | |
| type=Path, | |
| help="Write the generated continuation to this file", | |
| ) | |
| parser.add_argument( | |
| "--server", | |
| default="http://localhost:8080", | |
| help="llama.cpp server URL", | |
| ) | |
| parser.add_argument( | |
| "-n", | |
| "--n-predict", | |
| type=int, | |
| default=32, | |
| help="Maximum generated tokens", | |
| ) | |
| parser.add_argument( | |
| "--temperature", | |
| type=float, | |
| default=0.0, | |
| ) | |
| parser.add_argument( | |
| "--top-k", | |
| type=int, | |
| default=1, | |
| ) | |
| parser.add_argument( | |
| "--top-p", | |
| type=float, | |
| default=1.0, | |
| ) | |
| parser.add_argument( | |
| "--seed", | |
| type=int, | |
| default=42, | |
| ) | |
| parser.add_argument( | |
| "--include-prompt", | |
| action="store_true", | |
| help="Include the original prompt before the completion", | |
| ) | |
| return parser.parse_args() | |
| def main() -> int: | |
| args = parse_args() | |
| try: | |
| prompt = args.prompt_file.read_text(encoding="utf-8") | |
| except OSError as exc: | |
| print(f"Could not read prompt file: {exc}", file=sys.stderr) | |
| return 1 | |
| try: | |
| response = requests.post( | |
| f"{args.server.rstrip('/')}/completion", | |
| json={ | |
| "prompt": prompt, | |
| "n_predict": args.n_predict, | |
| "temperature": args.temperature, | |
| "top_k": args.top_k, | |
| "top_p": args.top_p, | |
| "seed": args.seed, | |
| "repeat_penalty": 1.0, | |
| "stream": False, | |
| }, | |
| timeout=3600, | |
| ) | |
| response.raise_for_status() | |
| except requests.RequestException as exc: | |
| print(f"llama.cpp request failed: {exc}", file=sys.stderr) | |
| return 1 | |
| result = response.json() | |
| completion = result.get("content") | |
| if completion is None: | |
| print(f"Unexpected response: {result}", file=sys.stderr) | |
| return 1 | |
| text = prompt + completion if args.include_prompt else completion | |
| if args.output: | |
| args.output.write_text(text, encoding="utf-8") | |
| else: | |
| sys.stdout.write(text) | |
| sys.stdout.flush() | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |