Instructions to use huggingtweets/vanpelt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/vanpelt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/vanpelt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/vanpelt") model = AutoModelForCausalLM.from_pretrained("huggingtweets/vanpelt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use huggingtweets/vanpelt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/vanpelt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/vanpelt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/vanpelt
- SGLang
How to use huggingtweets/vanpelt 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/vanpelt" \ --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/vanpelt", "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/vanpelt" \ --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/vanpelt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/vanpelt with Docker Model Runner:
docker model run hf.co/huggingtweets/vanpelt
New model from https://wandb.ai/wandb/huggingtweets/runs/2bfgtsxu
Browse files- README.md +4 -4
- pytorch_model.bin +2 -2
- training_args.bin +1 -1
README.md
CHANGED
|
@@ -68,15 +68,15 @@ The model was trained on [@vanpelt's tweets](https://twitter.com/vanpelt).
|
|
| 68 |
</tbody>
|
| 69 |
</table>
|
| 70 |
|
| 71 |
-
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/
|
| 72 |
|
| 73 |
## Training procedure
|
| 74 |
|
| 75 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vanpelt's tweets.
|
| 76 |
|
| 77 |
-
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/
|
| 78 |
|
| 79 |
-
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/
|
| 80 |
|
| 81 |
## Intended uses & limitations
|
| 82 |
|
|
@@ -110,4 +110,4 @@ For more details, visit the project repository.
|
|
| 110 |
|
| 111 |
[](https://github.com/borisdayma/huggingtweets)
|
| 112 |
|
| 113 |
-
<!--- random size file
|
|
|
|
| 68 |
</tbody>
|
| 69 |
</table>
|
| 70 |
|
| 71 |
+
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oxi9b39/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
|
| 72 |
|
| 73 |
## Training procedure
|
| 74 |
|
| 75 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vanpelt's tweets.
|
| 76 |
|
| 77 |
+
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bfgtsxu) for full transparency and reproducibility.
|
| 78 |
|
| 79 |
+
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bfgtsxu/artifacts) is logged and versioned.
|
| 80 |
|
| 81 |
## Intended uses & limitations
|
| 82 |
|
|
|
|
| 110 |
|
| 111 |
[](https://github.com/borisdayma/huggingtweets)
|
| 112 |
|
| 113 |
+
<!--- random size file -->
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a9f09c6d52ebc7dc775e04ec01ebc199f55617f57af08d842192b3cacccda1ee
|
| 3 |
+
size 510406673
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1775
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:430453dbf6f831cef730e0d06787d80302a6cc7ab3c7437fcdae19bd8282a834
|
| 3 |
size 1775
|