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

Libraries

How to use huggingtweets/codeinecucumber with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huggingtweets/codeinecucumber with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/codeinecucumber

SGLang

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

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

boris commited on Nov 29, 2022

Commit

7ead284

1 Parent(s): 7f5de4b

New model from https://wandb.ai/wandb/huggingtweets/runs/szqhs6it

Browse files

Files changed (3) hide show

README.md +11 -11
pytorch_model.bin +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 language: en
-thumbnail: http://www.huggingtweets.com/codeinecucumber/1668981794646/predictions.png
 tags:
 - huggingtweets
 widget:
@@ -20,7 +20,7 @@ widget:
         </div>
     </div>
     <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
-    <div style="text-align: center; font-size: 16px; font-weight: 800">Gutted</div>
     <div style="text-align: center; font-size: 14px;">@codeinecucumber</div>
 </div>
@@ -38,24 +38,24 @@ To understand how the model was developed, check the [W&B report](https://wandb.
 ## Training data
-The model was trained on tweets from Gutted.
-| Data | Gutted |
 | --- | --- |
-| Tweets downloaded | 2005 |
-| Retweets | 272 |
-| Short tweets | 388 |
-| Tweets kept | 1345 |
-[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/167dtbv8/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 @codeinecucumber's tweets.
-Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mdtqfjq) for full transparency and reproducibility.
-At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mdtqfjq/artifacts) is logged and versioned.
 ## How to use

 ---
 language: en
+thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
 tags:
 - huggingtweets
 widget:
         </div>
     </div>
     <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Yay</div>
     <div style="text-align: center; font-size: 14px;">@codeinecucumber</div>
 </div>
 ## Training data
+The model was trained on tweets from Yay.
+| Data | Yay |
 | --- | --- |
+| Tweets downloaded | 2206 |
+| Retweets | 300 |
+| Short tweets | 433 |
+| Tweets kept | 1473 |
+[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28q4wxl6/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 @codeinecucumber's tweets.
+Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/szqhs6it) for full transparency and reproducibility.
+At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/szqhs6it/artifacts) is logged and versioned.
 ## How to use

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