Instructions to use appvoid/palmer-003-turbo-2401 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-003-turbo-2401 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-003-turbo-2401")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-003-turbo-2401")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-003-turbo-2401", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/palmer-003-turbo-2401 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-003-turbo-2401"
# 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-003-turbo-2401",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/palmer-003-turbo-2401

SGLang

How to use appvoid/palmer-003-turbo-2401 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-003-turbo-2401" \
    --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-003-turbo-2401",
		"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-003-turbo-2401" \
        --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-003-turbo-2401",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use appvoid/palmer-003-turbo-2401 with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-003-turbo-2401
```

palmer

a better base model

This model will continuosly be improved over time. The model is named as palmer-003-turbo-yearmonth formatting.

evaluation 🧪

note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals

   Model           ARC_C   HellaSwag  PIQA  Winogrande Average
palmer-001	     | 0.2807 | 0.5524 | 0.7106 | 0.5896 | 0.5333 |
palmer-003-turbo | 0.3106 | 0.5806 | 0.7247 | 0.5951 | 0.5527 |
p-003-turbo-2401 | 0.3114 | 0.5805 | 0.7258 | 0.5959 | 0.5534 | (this)
palmer-002       | 0.3242 | 0.5956 | 0.7345 | 0.5888 | 0.5607 |

This model is as good as tinyllama base while being half the size.

prompt 📝

no prompt 🚀

Note

As of today 1/4/2024 is still not possible to convert to gguf, see more here.

Downloads last month: 5

Safetensors

Model size

1B params

Tensor type

F32

F16

appvoid
/

palmer-003-turbo-2401

palmer

a better base model

evaluation 🧪

prompt 📝

Note

Dataset used to train appvoid/palmer-003-turbo-2401