Instructions to use afrideva/palmer-x-002-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/palmer-x-002-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/palmer-x-002-GGUF",
	filename="palmer-x-002.fp16.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 afrideva/palmer-x-002-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/palmer-x-002-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/palmer-x-002-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/palmer-x-002-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/palmer-x-002-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 afrideva/palmer-x-002-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/palmer-x-002-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 afrideva/palmer-x-002-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/palmer-x-002-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/palmer-x-002-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/palmer-x-002-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/palmer-x-002-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/palmer-x-002-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/palmer-x-002-GGUF:Q4_K_M

Ollama
How to use afrideva/palmer-x-002-GGUF with Ollama:
```
ollama run hf.co/afrideva/palmer-x-002-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/palmer-x-002-GGUF 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 afrideva/palmer-x-002-GGUF 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 afrideva/palmer-x-002-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/palmer-x-002-GGUF to start chatting

Docker Model Runner
How to use afrideva/palmer-x-002-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/palmer-x-002-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/palmer-x-002-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/palmer-x-002-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.palmer-x-002-GGUF-Q4_K_M

List all available models

lemonade list

appvoid/palmer-x-002-GGUF

Quantized GGUF model files for palmer-x-002 from appvoid

Name	Quant method	Size
palmer-x-002.fp16.gguf	fp16	2.20 GB
palmer-x-002.q2_k.gguf	q2_k	483.12 MB
palmer-x-002.q3_k_m.gguf	q3_k_m	550.82 MB
palmer-x-002.q4_k_m.gguf	q4_k_m	668.79 MB
palmer-x-002.q5_k_m.gguf	q5_k_m	783.02 MB
palmer-x-002.q6_k.gguf	q6_k	904.39 MB
palmer-x-002.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

x-002

This is an incremental model update on palmer-002 using dpo technique. X means dpo+sft spinoff.

evaluation

Model	ARC_C	HellaSwag	PIQA	Winogrande
tinyllama-2t	0.2807	0.5463	0.7067	0.5683
palmer-001	0.2807	0.5524	0.7106	0.5896
tinyllama-2.5t	0.3191	0.5896	0.7307	0.5872
palmer-002	0.3242	0.5956	0.7345	0.5888
palmer-x-002	0.3224	0.5941	0.7383	0.5912

training

~500 dpo samples as experimental data to check on improvements. It seems like data is making it better on some benchmarks while also degrading quality on others.

prompt

no prompt

As you can notice, the model actually completes by default questions that are the most-likely to be asked, which is good because most people will use it to answer as a chatbot.

Downloads last month: 30

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

afrideva
/

palmer-x-002-GGUF