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

Libraries

How to use Harshatheeswar/babylama-gpttoken with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Harshatheeswar/babylama-gpttoken with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harshatheeswar/babylama-gpttoken"
# 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-gpttoken",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

babylama-gpttoken

This model is a fine-tuned version of babylm/babyllama-100m-2024 on the None dataset. It achieves the following results on the evaluation set:

Loss: 38.2921

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
30.4256	0.9999	5559	29.8987
38.306	1.9999	11119	37.7677
38.7146	3.0000	16679	37.7375
37.8024	4.0	22239	38.2709
37.35	4.9993	27795	38.2921

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

93.4M params

Tensor type

F32

Model tree for Harshatheeswar/babylama-gpttoken

Base model

babylm/babyllama-100m-2024

Finetuned

(6)

this model