Instructions to use Mayuresh231/tiny-transformer-29m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mayuresh231/tiny-transformer-29m with Transformers:

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

pipe = pipeline("text-generation", model="Mayuresh231/tiny-transformer-29m", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Mayuresh231/tiny-transformer-29m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mayuresh231/tiny-transformer-29m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mayuresh231/tiny-transformer-29m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mayuresh231/tiny-transformer-29m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mayuresh231/tiny-transformer-29m

SGLang

How to use Mayuresh231/tiny-transformer-29m 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 "Mayuresh231/tiny-transformer-29m" \
    --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": "Mayuresh231/tiny-transformer-29m",
		"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 "Mayuresh231/tiny-transformer-29m" \
        --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": "Mayuresh231/tiny-transformer-29m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mayuresh231/tiny-transformer-29m with Docker Model Runner:
```
docker model run hf.co/Mayuresh231/tiny-transformer-29m
```

tiny-transformer-29m / config.json

Mayuresh231

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{
  "architectures": [
    "TinyTransformerForCausalLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "auto_map": {
    "AutoConfig": "configuration_tiny_transformer.TinyTransformerConfig",
    "AutoModelForCausalLM": "modeling_tiny_transformer.TinyTransformerForCausalLM"
  },
  "bos_token_id": 2,
  "context": 256,
  "dim": 512,
  "dropout": 0.1,
  "dtype": "float32",
  "eos_token_id": 3,
  "ff_dim": 2048,
  "heads": 8,
  "hidden_dropout_prob": 0.1,
  "hidden_size": 512,
  "intermediate_size": 2048,
  "layers": 8,
  "max_position_embeddings": 256,
  "model_type": "tiny_transformer",
  "num_attention_heads": 8,
  "num_hidden_layers": 8,
  "pad_token_id": 0,
  "tie_word_embeddings": false,
  "transformers_version": "5.16.1",
  "unk_token_id": 1,
  "use_cache": false,
  "vocab_size": 4096
}

	{
	"architectures": [
	"TinyTransformerForCausalLM"
	],
	"attention_probs_dropout_prob": 0.1,
	"auto_map": {
	"AutoConfig": "configuration_tiny_transformer.TinyTransformerConfig",
	"AutoModelForCausalLM": "modeling_tiny_transformer.TinyTransformerForCausalLM"
	},
	"bos_token_id": 2,
	"context": 256,
	"dim": 512,
	"dropout": 0.1,
	"dtype": "float32",
	"eos_token_id": 3,
	"ff_dim": 2048,
	"heads": 8,
	"hidden_dropout_prob": 0.1,
	"hidden_size": 512,
	"intermediate_size": 2048,
	"layers": 8,
	"max_position_embeddings": 256,
	"model_type": "tiny_transformer",
	"num_attention_heads": 8,
	"num_hidden_layers": 8,
	"pad_token_id": 0,
	"tie_word_embeddings": false,
	"transformers_version": "5.16.1",
	"unk_token_id": 1,
	"use_cache": false,
	"vocab_size": 4096
	}