Instructions to use nikitastheo/babylm-eng-ell-sequential_interleaved-dummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikitastheo/babylm-eng-ell-sequential_interleaved-dummy with Transformers:

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

pipe = pipeline("text-generation", model="nikitastheo/babylm-eng-ell-sequential_interleaved-dummy")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nikitastheo/babylm-eng-ell-sequential_interleaved-dummy")
model = AutoModelForCausalLM.from_pretrained("nikitastheo/babylm-eng-ell-sequential_interleaved-dummy")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nikitastheo/babylm-eng-ell-sequential_interleaved-dummy with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nikitastheo/babylm-eng-ell-sequential_interleaved-dummy

SGLang

How to use nikitastheo/babylm-eng-ell-sequential_interleaved-dummy 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 "nikitastheo/babylm-eng-ell-sequential_interleaved-dummy" \
    --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": "nikitastheo/babylm-eng-ell-sequential_interleaved-dummy",
		"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 "nikitastheo/babylm-eng-ell-sequential_interleaved-dummy" \
        --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": "nikitastheo/babylm-eng-ell-sequential_interleaved-dummy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nikitastheo/babylm-eng-ell-sequential_interleaved-dummy with Docker Model Runner:
```
docker model run hf.co/nikitastheo/babylm-eng-ell-sequential_interleaved-dummy
```

nikitastheo/babylm-eng-ell-sequential_interleaved-dummy

Trained with train_clm.py, a Hugging Face Accelerate causal-LM training script (no Trainer).

Training details

Base config: gpt_dummy_config.json
Tokenizer: nikitastheo/babylm-eng-ell-tokenizer
Max steps: 12
Learning rate: 0.0001
LR scheduler: linear
Warmup steps: 16
Batch size (per device): 32
Gradient accumulation steps: 1
Total train batch size: 32
Language switch epoch: 3

Downloads last month: 34

Safetensors

Model size

3.41M params

Tensor type

F32