Instructions to use rgeada/tiny-untrained-granite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rgeada/tiny-untrained-granite with Transformers:

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

pipe = pipeline("text-generation", model="rgeada/tiny-untrained-granite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rgeada/tiny-untrained-granite")
model = AutoModelForCausalLM.from_pretrained("rgeada/tiny-untrained-granite", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rgeada/tiny-untrained-granite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rgeada/tiny-untrained-granite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rgeada/tiny-untrained-granite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rgeada/tiny-untrained-granite

SGLang

How to use rgeada/tiny-untrained-granite 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 "rgeada/tiny-untrained-granite" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rgeada/tiny-untrained-granite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "rgeada/tiny-untrained-granite" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rgeada/tiny-untrained-granite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rgeada/tiny-untrained-granite with Docker Model Runner:
```
docker model run hf.co/rgeada/tiny-untrained-granite
```

rgeada commited on Apr 14, 2025

Commit

86e6558

verified ·

1 Parent(s): 5d0ce84

Upload GraniteForCausalLM

Browse files

Files changed (2) hide show

config.json +1 -1
model.safetensors +2 -2

config.json CHANGED Viewed

@@ -17,7 +17,7 @@
   "mlp_bias": false,
   "model_type": "granite",
   "num_attention_heads": 1,
-  "num_hidden_layers": 1,
   "num_key_value_heads": 1,
   "residual_multiplier": 1.0,
   "rms_norm_eps": 1e-06,

   "mlp_bias": false,
   "model_type": "granite",
   "num_attention_heads": 1,
+  "num_hidden_layers": 4,
   "num_key_value_heads": 1,
   "residual_multiplier": 1.0,
   "rms_norm_eps": 1e-06,

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ac58ee6836428151714d393c42c36edde2d03d6fdea65da43120c115e133bdf1
-size 25235696

 version https://git-lfs.github.com/spec/v1
+oid sha256:100485ce68920735fed414adc6abcabdb6765d2a63e9fca68fe009dd98842e8b
+size 25439024