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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("layai/syn-dataaug-youtube-context")
model = AutoModelForCausalLM.from_pretrained("layai/syn-dataaug-youtube-context", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

context

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: 3.7005
Accuracy: 0.4548

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
0.7716	0.6479	500	3.7005	0.4548
0.3675	1.2958	1000	3.8487	0.4565
0.2102	1.9436	1500	3.9705	0.4568
0.1164	2.5915	2000	4.1546	0.4570

Framework versions

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

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

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

Base model

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

Finetuned

(607)

this model

Collection including layai/syn-dataaug-youtube-context

Syn Youtube Data-Aug

Collection

3 items • Updated May 11