Instructions to use mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch with Transformers:

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

pipe = pipeline("text-generation", model="mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch

SGLang

How to use mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch 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 "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch" \
    --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": "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch",
		"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 "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch" \
        --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": "mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch with Docker Model Runner:
```
docker model run hf.co/mlnomad/gelu-d12-chinchilla-261M-seed2-pytorch
```

gelu-d12-chinchilla-261M-seed2-pytorch

File size: 698 Bytes

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{
  "architectures": [
    "GeluGPTForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_gelu_gpt.GeluGPTConfig",
    "AutoModelForCausalLM": "modeling_gelu_gpt.GeluGPTForCausalLM"
  },
  "dtype": "float32",
  "hidden_size": 768,
  "max_position_embeddings": 10240,
  "mlp": "gelu",
  "model_type": "gelu_gpt",
  "n_embd": 768,
  "n_head": 12,
  "n_kv_head": 12,
  "n_layer": 12,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "num_key_value_heads": 12,
  "pad_vocab_size_to": 64,
  "rope_base": 100000.0,
  "sequence_len": 1024,
  "tie_embeddings": true,
  "tie_word_embeddings": true,
  "transformers_version": "5.5.0",
  "vocab_size": 32768,
  "window_pattern": "SSSL"
}