Instructions to use huggingtweets/chamath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/chamath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/chamath")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/chamath") model = AutoModelForCausalLM.from_pretrained("huggingtweets/chamath") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use huggingtweets/chamath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/chamath" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/chamath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/chamath
- SGLang
How to use huggingtweets/chamath 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/chamath" \ --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/chamath", "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/chamath" \ --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/chamath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/chamath with Docker Model Runner:
docker model run hf.co/huggingtweets/chamath
New model from https://wandb.ai/wandb/huggingtweets/runs/3509dnak
Browse files- README.md +6 -6
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
-
thumbnail: https://
|
| 4 |
tags:
|
| 5 |
- huggingtweets
|
| 6 |
widget:
|
|
@@ -33,19 +33,19 @@ The model was trained on [@chamath's tweets](https://twitter.com/chamath).
|
|
| 33 |
| Data | Quantity |
|
| 34 |
| --- | --- |
|
| 35 |
| Tweets downloaded | 3246 |
|
| 36 |
-
| Retweets |
|
| 37 |
| Short tweets | 737 |
|
| 38 |
-
| Tweets kept |
|
| 39 |
|
| 40 |
-
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/
|
| 41 |
|
| 42 |
## Training procedure
|
| 43 |
|
| 44 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chamath's tweets.
|
| 45 |
|
| 46 |
-
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/
|
| 47 |
|
| 48 |
-
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/
|
| 49 |
|
| 50 |
## How to use
|
| 51 |
|
|
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
+
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
|
| 4 |
tags:
|
| 5 |
- huggingtweets
|
| 6 |
widget:
|
|
|
|
| 33 |
| Data | Quantity |
|
| 34 |
| --- | --- |
|
| 35 |
| Tweets downloaded | 3246 |
|
| 36 |
+
| Retweets | 145 |
|
| 37 |
| Short tweets | 737 |
|
| 38 |
+
| Tweets kept | 2364 |
|
| 39 |
|
| 40 |
+
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lw15im3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
|
| 41 |
|
| 42 |
## Training procedure
|
| 43 |
|
| 44 |
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chamath's tweets.
|
| 45 |
|
| 46 |
+
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3509dnak) for full transparency and reproducibility.
|
| 47 |
|
| 48 |
+
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3509dnak/artifacts) is logged and versioned.
|
| 49 |
|
| 50 |
## How to use
|
| 51 |
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 510408315
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9abbfcdd05acd60487abf74b4cb75b083e5f8b36df494354ff8fc2c9305672aa
|
| 3 |
size 510408315
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2351
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ae135050b0b83e13ff305a2ec39788124711cbb39047f3acac93272faeed0a85
|
| 3 |
size 2351
|