Instructions to use appvoid/palmer-004 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-004 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-004")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-004")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-004")

llama-cpp-python

How to use appvoid/palmer-004 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="appvoid/palmer-004",
	filename="2406-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use appvoid/palmer-004 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf appvoid/palmer-004:F16
# Run inference directly in the terminal:
llama-cli -hf appvoid/palmer-004:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf appvoid/palmer-004:F16
# Run inference directly in the terminal:
llama-cli -hf appvoid/palmer-004:F16

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-004:F16
# Run inference directly in the terminal:
./llama-cli -hf appvoid/palmer-004:F16

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-004:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf appvoid/palmer-004:F16

Use Docker

docker model run hf.co/appvoid/palmer-004:F16

LM Studio
Jan

vLLM

How to use appvoid/palmer-004 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-004"
# 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-004",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/palmer-004:F16

SGLang

How to use appvoid/palmer-004 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-004" \
    --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-004",
		"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-004" \
        --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-004",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use appvoid/palmer-004 with Ollama:
```
ollama run hf.co/appvoid/palmer-004:F16
```

Unsloth Studio

How to use appvoid/palmer-004 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-004 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-004 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-004 to start chatting

Docker Model Runner
How to use appvoid/palmer-004 with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-004:F16
```

Lemonade

How to use appvoid/palmer-004 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/palmer-004:F16

Run and chat with the model

lemonade run user.palmer-004-F16

List all available models

lemonade list

	
		
	
	
		june update
	
This model has improved overall performance at the expense of small degradation on winogrande. As all palmer models, the model is biased to respond to answers without using any specific prompt, feel free to further fine-tune it for your specific use case.

	
		
Model
MMLU
ARC-C
HellaSwag
PIQA
Winogrande
Average

tinyllama-3t
0.2577
0.3029
0.5935
0.7329
0.5959
0.4966

palmer-004-old
0.2601
0.3456
0.6138
0.7443
0.6511
0.5229

palmer-004
0.2661
0.3490
0.6173
0.7481
0.6417
0.5244

	

	
		
	
	
		note
	
Even though palmer-003 is only 2k context size, it's 0.5257 on average so if you don't need that much context size (32k) you are better off with the former one.

Model	MMLU	ARC-C	HellaSwag	PIQA	Winogrande	Average
tinyllama-3t	0.2577	0.3029	0.5935	0.7329	0.5959	0.4966
palmer-004-old	0.2601	0.3456	0.6138	0.7443	0.6511	0.5229
palmer-004	0.2661	0.3490	0.6173	0.7481	0.6417	0.5244

Downloads last month: 14

Safetensors

Model size

1B params

Tensor type

F16

Model tree for appvoid/palmer-004

appvoid/palmer-002-32k

appvoid/palmer-003

raidhon/coven_tiny_1.1b_32k_orpo_alpha

Merge model

this model

Merges

5 models