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

Libraries

How to use huggingtweets/nicolasmaduro with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("huggingtweets/nicolasmaduro")
model = AutoModelForCausalLM.from_pretrained("huggingtweets/nicolasmaduro")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use huggingtweets/nicolasmaduro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/nicolasmaduro

SGLang

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

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

boris commited on Jan 20, 2021

Commit

faf18b7

1 Parent(s): 828c052

New model from https://wandb.ai/wandb/huggingtweets/runs/3r7whlkd

Browse files

Files changed (3) hide show

README.md +3 -3
pytorch_model.bin +2 -2
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -68,15 +68,15 @@ The model was trained on [@nicolasmaduro's tweets](https://twitter.com/nicolasma
 </tbody>
 </table>
-[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ne8h0ti/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
 ## Training procedure
 The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nicolasmaduro's tweets.
-Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ufcisio) for full transparency and reproducibility.
-At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ufcisio/artifacts) is logged and versioned.
 ## Intended uses & limitations

 </tbody>
 </table>
+[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gqdgmie/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
 ## Training procedure
 The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nicolasmaduro's tweets.
+Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3r7whlkd) for full transparency and reproducibility.
+At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3r7whlkd/artifacts) is logged and versioned.
 ## Intended uses & limitations

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:992a627f91600f207692cc503e4dd4655ec4dfae72ad7fd25aacd464d20ee859
-size 510406770

 version https://git-lfs.github.com/spec/v1
+oid sha256:0f62f79b8dc15973cb7fdbc9ef3a3e60a1733bd12e1abde31acb038574fd9bf8
+size 510406556

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3b4a6cf53563b996d48c8f9879e21d29a644c7e28653cbf690edb01377ce49cb
 size 2031

 version https://git-lfs.github.com/spec/v1
+oid sha256:f47d265e61d8edd2c566d420a1d8bc96e84ce8c57a9b852a85b06c2e2909d56b
 size 2031