Instructions to use bigmorning/try-m-e-perplexity594 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bigmorning/try-m-e-perplexity594 with Transformers:

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

pipe = pipeline("text-generation", model="bigmorning/try-m-e-perplexity594")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bigmorning/try-m-e-perplexity594")
model = AutoModelForCausalLM.from_pretrained("bigmorning/try-m-e-perplexity594")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bigmorning/try-m-e-perplexity594 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bigmorning/try-m-e-perplexity594"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigmorning/try-m-e-perplexity594",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bigmorning/try-m-e-perplexity594

SGLang

How to use bigmorning/try-m-e-perplexity594 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 "bigmorning/try-m-e-perplexity594" \
    --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": "bigmorning/try-m-e-perplexity594",
		"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 "bigmorning/try-m-e-perplexity594" \
        --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": "bigmorning/try-m-e-perplexity594",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bigmorning/try-m-e-perplexity594 with Docker Model Runner:
```
docker model run hf.co/bigmorning/try-m-e-perplexity594
```

Update README.md

by xiwisok - opened Jan 19, 2024

base: refs/heads/main

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from: refs/pr/2

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@@ -5,6 +5,8 @@ tags:
 model-index:
 - name: try-m-e-perplexity594
   results: []
 ---
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -45,4 +47,4 @@ The following hyperparameters were used during training:
 - Transformers 4.17.0
 - TensorFlow 2.8.0
 - Datasets 2.0.0
-- Tokenizers 0.11.6

 model-index:
 - name: try-m-e-perplexity594
   results: []
+language:
+- en
 ---
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 - Transformers 4.17.0
 - TensorFlow 2.8.0
 - Datasets 2.0.0
+- Tokenizers 0.11.6