Instructions to use Lakoc/TED_CLM_gpt2_tedlium4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lakoc/TED_CLM_gpt2_tedlium4 with Transformers:

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

pipe = pipeline("text-generation", model="Lakoc/TED_CLM_gpt2_tedlium4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lakoc/TED_CLM_gpt2_tedlium4")
model = AutoModelForCausalLM.from_pretrained("Lakoc/TED_CLM_gpt2_tedlium4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lakoc/TED_CLM_gpt2_tedlium4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lakoc/TED_CLM_gpt2_tedlium4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lakoc/TED_CLM_gpt2_tedlium4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lakoc/TED_CLM_gpt2_tedlium4

SGLang

How to use Lakoc/TED_CLM_gpt2_tedlium4 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 "Lakoc/TED_CLM_gpt2_tedlium4" \
    --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": "Lakoc/TED_CLM_gpt2_tedlium4",
		"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 "Lakoc/TED_CLM_gpt2_tedlium4" \
        --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": "Lakoc/TED_CLM_gpt2_tedlium4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lakoc/TED_CLM_gpt2_tedlium4 with Docker Model Runner:
```
docker model run hf.co/Lakoc/TED_CLM_gpt2_tedlium4
```

TED_CLM_gpt2_tedlium4

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.9157
Accuracy: 0.5497

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.005
train_batch_size: 128
eval_batch_size: 128
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 512
total_eval_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20000
num_epochs: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1599	0.32	3000	2.3060	0.4711
2.0452	0.65	6000	2.1564	0.5013
1.9981	0.97	9000	2.0995	0.5142
1.9806	1.3	12000	2.0596	0.5168
1.9459	1.62	15000	2.0307	0.5235
1.9205	1.95	18000	2.0189	0.5279
1.9039	2.27	21000	2.0024	0.5313
1.9033	2.6	24000	1.9890	0.5337
1.8621	2.92	27000	1.9509	0.5401
1.8252	3.25	30000	1.9420	0.5439
1.813	3.57	33000	1.9208	0.5493
1.8085	3.9	36000	1.9157	0.5497
1.7984	4.22	39000	1.9157	0.5497
1.8008	4.55	42000	1.9157	0.5497
1.7967	4.87	45000	1.9157	0.5497
1.7972	5.2	48000	1.9157	0.5497
1.7967	5.52	51000	1.9157	0.5497

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.13.1
Tokenizers 0.13.3

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