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

Libraries

How to use appvoid/palmer-004-original with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-004-original")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-004-original", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/palmer-004-original with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/appvoid/palmer-004-original

SGLang

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

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

appvoid commited on Jun 1, 2024

Commit

e3dc942

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Update README.md

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@@ -27,9 +27,11 @@ Crafted using the TIES merge method, palmer-004 elevates its predecessor's prowe
 #### Performance
-  Model         MMLU     ARC-C  HellaSwag  PIQA  Winogrande
-tinyllama-3t | 0.2577 | 0.3029 | 0.5935 | 0.7329 | 0.5959 |
-palmer-003   | 0.2523 | 0.3439 | 0.6208 | 0.7524 | 0.6590 |
-palmer-004   | 0.2601 | 0.3456 | 0.6138 | 0.7443 | 0.6511 |
 </div>

 #### Performance
+| Model         | MMLU  | ARC-C | HellaSwag | PIQA   | Winogrande |
+|---------------|-------|-------|-----------|--------|------------|
+| tinyllama-3t  | 0.2577| 0.3029| 0.5935    | 0.7329 | 0.5959     |
+| palmer-003    | 0.2523| 0.3439| 0.6208    | 0.7524 | 0.6590     |
+| palmer-004    | 0.2601| 0.3456| 0.6138    | 0.7443 | 0.6511     |
 </div>