Instructions to use Harshatheeswar/babylama-hidden_layers12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harshatheeswar/babylama-hidden_layers12 with Transformers:

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

pipe = pipeline("text-generation", model="Harshatheeswar/babylama-hidden_layers12")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Harshatheeswar/babylama-hidden_layers12")
model = AutoModelForCausalLM.from_pretrained("Harshatheeswar/babylama-hidden_layers12")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Harshatheeswar/babylama-hidden_layers12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harshatheeswar/babylama-hidden_layers12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harshatheeswar/babylama-hidden_layers12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Harshatheeswar/babylama-hidden_layers12

SGLang

How to use Harshatheeswar/babylama-hidden_layers12 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 "Harshatheeswar/babylama-hidden_layers12" \
    --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": "Harshatheeswar/babylama-hidden_layers12",
		"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 "Harshatheeswar/babylama-hidden_layers12" \
        --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": "Harshatheeswar/babylama-hidden_layers12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Harshatheeswar/babylama-hidden_layers12 with Docker Model Runner:
```
docker model run hf.co/Harshatheeswar/babylama-hidden_layers12
```

babylama-hidden_layers12

This model is a fine-tuned version of Harshatheeswar/babylama-hidden_sizes768 on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.8091

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.1221	0.9999	5559	4.1310
3.776	1.9999	11119	3.8951
3.5749	2.9998	16678	3.8044
3.4408	3.9999	22238	3.7917
3.3023	4.9994	27795	3.8091

Framework versions

Transformers 4.46.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Harshatheeswar/babylama-hidden_layers12

Base model

babylm/babyllama-100m-2024

Finetuned

Harshatheeswar/babylama-hidden_sizes768

Finetuned

(2)

this model