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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

syn-dataaug-youtube-context / README.md

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	---
	library_name: transformers
	base_model: meta-llama/Meta-Llama-3-8B
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: context
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# context

	This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co//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