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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("layai/syn-dataaug-combined-context")
model = AutoModelForCausalLM.from_pretrained("layai/syn-dataaug-combined-context")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

syn-dataaug-combined-context / README.md

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metadata

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

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: 5.1785
Accuracy: 0.2448

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
1.276	0.8306	500	5.1785	0.2448
0.5321	1.6611	1000	5.2637	0.2565
0.3538	2.4917	1500	5.4336	0.2552

Framework versions

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