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

Libraries

How to use huggingtweets/marknorm with Transformers:

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huggingtweets/marknorm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/marknorm

SGLang

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

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

boris commited on Dec 10, 2020

Commit

8a8a9b1

1 Parent(s): af6c35c

New model from https://wandb.ai/wandb/huggingtweets/runs/1ezcon9h

Browse files

Files changed (3) hide show

README.md +8 -8
pytorch_model.bin +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 language: en
-thumbnail: https://www.huggingtweets.com/marknorm/1607138531875/predictions.png
 tags:
 - huggingtweets
 widget:
@@ -51,32 +51,32 @@ The model was trained on [@marknorm's tweets](https://twitter.com/marknorm).
 <tbody style='border-width:0'>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Tweets downloaded</td>
-<td style='border-width:0'>3223</td>
 </tr>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Retweets</td>
-<td style='border-width:0'>167</td>
 </tr>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Short tweets</td>
-<td style='border-width:0'>584</td>
 </tr>
 <tr style='border-width:0'>
 <td style='border-width:0'>Tweets kept</td>
-<td style='border-width:0'>2472</td>
 </tr>
 </tbody>
 </table>
-[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20nen2js/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 @marknorm's tweets.
-Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19kx6tuu) for full transparency and reproducibility.
-At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19kx6tuu/artifacts) is logged and versioned.
 ## Intended uses & limitations

 ---
 language: en
+thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
 tags:
 - huggingtweets
 widget:
 <tbody style='border-width:0'>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Tweets downloaded</td>
+<td style='border-width:0'>3199</td>
 </tr>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Retweets</td>
+<td style='border-width:0'>164</td>
 </tr>
 <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
 <td style='border-width:0'>Short tweets</td>
+<td style='border-width:0'>576</td>
 </tr>
 <tr style='border-width:0'>
 <td style='border-width:0'>Tweets kept</td>
+<td style='border-width:0'>2459</td>
 </tr>
 </tbody>
 </table>
+[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wvjtg55/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 @marknorm's tweets.
+Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ezcon9h) for full transparency and reproducibility.
+At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ezcon9h/artifacts) is logged and versioned.
 ## Intended uses & limitations

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