Instructions to use huggingtweets/hamletbatista with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huggingtweets/hamletbatista with Transformers:

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

pipe = pipeline("text-generation", model="huggingtweets/hamletbatista")

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

tokenizer = AutoTokenizer.from_pretrained("huggingtweets/hamletbatista")
model = AutoModelForCausalLM.from_pretrained("huggingtweets/hamletbatista", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huggingtweets/hamletbatista with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/hamletbatista

SGLang

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

Docker Model Runner
How to use huggingtweets/hamletbatista with Docker Model Runner:
```
docker model run hf.co/huggingtweets/hamletbatista
```

hamletbatista

Commit History

May 22, 2021

06:31
upload flax model

patrickvonplaten

d4e2438
06:30
allow flax

patrickvonplaten

83cceb8

Sep 23, 2020

11:07
Update README.md

system HF Staff

c36f092
11:07
Update README.md

system HF Staff

0343c97
11:07
Update README.md

system HF Staff

807bf6c
11:07
Update README.md

system HF Staff

38f5150
11:01
Update README.md

system HF Staff

7436d46
10:53
Update pytorch_model.bin

system HF Staff

f4cef57
10:53
Update vocab.json

system HF Staff

fa96bdf
10:53
Update special_tokens_map.json

system HF Staff

c8a9e2e
10:53
Update README.md

system HF Staff

95f80c5
10:53
Update merges.txt

system HF Staff

7a6edd9
10:53
Update tokenizer_config.json

system HF Staff

192960f
10:53
Update config.json

system HF Staff

6033f12
10:53
initial commit

system HF Staff

9adbd76

Commit History

May 22, 2021

upload flax model

allow flax

Sep 23, 2020

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update pytorch_model.bin

Update vocab.json

Update special_tokens_map.json

Update README.md

Update merges.txt

Update tokenizer_config.json

Update config.json

initial commit