Instructions to use huggingtweets/burdeevt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/burdeevt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/burdeevt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/burdeevt") model = AutoModelForCausalLM.from_pretrained("huggingtweets/burdeevt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use huggingtweets/burdeevt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/burdeevt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/burdeevt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/burdeevt
- SGLang
How to use huggingtweets/burdeevt 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/burdeevt" \ --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/burdeevt", "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/burdeevt" \ --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/burdeevt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/burdeevt with Docker Model Runner:
docker model run hf.co/huggingtweets/burdeevt
New model from https://wandb.ai/wandb/huggingtweets/runs/2t35juo3
Browse files- README.md +4 -4
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
-
thumbnail:
|
| 4 |
tags:
|
| 5 |
- huggingtweets
|
| 6 |
widget:
|
|
@@ -47,15 +47,15 @@ The model was trained on tweets from Burdee 🐣💖.
|
|
| 47 |
| Short tweets | 252 |
|
| 48 |
| Tweets kept | 560 |
|
| 49 |
|
| 50 |
-
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/
|
| 51 |
|
| 52 |
## Training procedure
|
| 53 |
|
| 54 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @burdeevt's tweets.
|
| 55 |
|
| 56 |
-
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/
|
| 57 |
|
| 58 |
-
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/
|
| 59 |
|
| 60 |
## How to use
|
| 61 |
|
|
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
+
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
|
| 4 |
tags:
|
| 5 |
- huggingtweets
|
| 6 |
widget:
|
|
|
|
| 47 |
| Short tweets | 252 |
|
| 48 |
| Tweets kept | 560 |
|
| 49 |
|
| 50 |
+
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37eoz4i5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
|
| 51 |
|
| 52 |
## Training procedure
|
| 53 |
|
| 54 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @burdeevt's tweets.
|
| 55 |
|
| 56 |
+
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t35juo3) for full transparency and reproducibility.
|
| 57 |
|
| 58 |
+
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t35juo3/artifacts) is logged and versioned.
|
| 59 |
|
| 60 |
## How to use
|
| 61 |
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 510396521
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5cbbe125a73dabcf60116bff60e52a6bc7a8f4cbf30fd7a8f44ceb5ebb559874
|
| 3 |
size 510396521
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3311
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3258f79ee873f25c4ae1e5d41c2beb2a8d7dfad35d687e6d7ea8e36c7e236d92
|
| 3 |
size 3311
|