Instructions to use layai/syn-dataaug-youtube-dict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use layai/syn-dataaug-youtube-dict with Transformers:

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

pipe = pipeline("text-generation", model="layai/syn-dataaug-youtube-dict")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("layai/syn-dataaug-youtube-dict")
model = AutoModelForCausalLM.from_pretrained("layai/syn-dataaug-youtube-dict")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use layai/syn-dataaug-youtube-dict with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/layai/syn-dataaug-youtube-dict

SGLang

How to use layai/syn-dataaug-youtube-dict 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 "layai/syn-dataaug-youtube-dict" \
    --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": "layai/syn-dataaug-youtube-dict",
		"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 "layai/syn-dataaug-youtube-dict" \
        --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": "layai/syn-dataaug-youtube-dict",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use layai/syn-dataaug-youtube-dict with Docker Model Runner:
```
docker model run hf.co/layai/syn-dataaug-youtube-dict
```

dict

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.6060
Accuracy: 0.4585

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: 5e-05
train_batch_size: 40
eval_batch_size: 40
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 160
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7536	0.6439	500	3.6060	0.4585
0.3454	1.2878	1000	3.9088	0.4632
0.2247	1.9317	1500	3.9477	0.4596
0.1506	2.5757	2000	4.0823	0.4569

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 11

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for layai/syn-dataaug-youtube-dict

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

(591)

this model

Collection including layai/syn-dataaug-youtube-dict

Syn Youtube Data-Aug

Collection

3 items • Updated 21 days ago