Instructions to use abinayam/gpt-2-tamil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abinayam/gpt-2-tamil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abinayam/gpt-2-tamil")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abinayam/gpt-2-tamil") model = AutoModelForCausalLM.from_pretrained("abinayam/gpt-2-tamil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abinayam/gpt-2-tamil with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abinayam/gpt-2-tamil" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abinayam/gpt-2-tamil", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abinayam/gpt-2-tamil
- SGLang
How to use abinayam/gpt-2-tamil 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 "abinayam/gpt-2-tamil" \ --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": "abinayam/gpt-2-tamil", "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 "abinayam/gpt-2-tamil" \ --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": "abinayam/gpt-2-tamil", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abinayam/gpt-2-tamil with Docker Model Runner:
docker model run hf.co/abinayam/gpt-2-tamil
AbinayaM02 commited on
Commit ·
a8c2f96
1
Parent(s): ed4e9b8
Updated model
Browse files
gpt-2-tamil/flax_model.msgpack
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 497764120
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a2d3c0a2be67a4eaacda46f9a3390f57fa5997222765b6bea6d3bd8c1dcc9b87
|
| 3 |
size 497764120
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 510401385
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3c0ee85ffb035131c4f355280c4e88dcf3c39521839d8041fcf8591eccb67e57
|
| 3 |
size 510401385
|
scripts/wandb/run-20210715_092837-watdq7ib/files/wandb-summary.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"global_step":
|
|
|
|
| 1 |
+
{"global_step": 161500, "_timestamp": 1626514606.490128, "train_time": 3028239.5, "train_learning_rate": 2.2478377559309592e-06, "_step": 322031, "train_loss": 1.1271708011627197, "eval_loss": 1.1323037147521973, "eval_perplexity": 3.1027963161468506}
|