Instructions to use onnx-internal-testing/tiny-random-LlamaForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onnx-internal-testing/tiny-random-LlamaForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="onnx-internal-testing/tiny-random-LlamaForCausalLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("onnx-internal-testing/tiny-random-LlamaForCausalLM")
model = AutoModelForCausalLM.from_pretrained("onnx-internal-testing/tiny-random-LlamaForCausalLM", 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 onnx-internal-testing/tiny-random-LlamaForCausalLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/onnx-internal-testing/tiny-random-LlamaForCausalLM

SGLang

How to use onnx-internal-testing/tiny-random-LlamaForCausalLM 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 "onnx-internal-testing/tiny-random-LlamaForCausalLM" \
    --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": "onnx-internal-testing/tiny-random-LlamaForCausalLM",
		"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 "onnx-internal-testing/tiny-random-LlamaForCausalLM" \
        --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": "onnx-internal-testing/tiny-random-LlamaForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use onnx-internal-testing/tiny-random-LlamaForCausalLM with Docker Model Runner:
```
docker model run hf.co/onnx-internal-testing/tiny-random-LlamaForCausalLM
```

Xenova HF Staff commited on Jul 10, 2025

Commit

2715498

verified ·

1 Parent(s): db1fc70

Update README.md

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@@ -7,6 +7,31 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details

 <!-- Provide a quick summary of what the model is/does. -->
+## Code to generate
+```py
+from transformers import AutoConfig, LlamaForCausalLM, AutoTokenizer
+import torch
+model_id='meta-llama/Llama-3.2-1B-Instruct'
+config = AutoConfig.from_pretrained(
+    model_id,
+    head_dim=8,
+    hidden_size=32,
+    num_attention_heads=4,
+    num_key_value_heads=2,
+    num_hidden_layers=2,
+    intermediate_size=64,
+)
+model = LlamaForCausalLM(config)
+torch.manual_seed(0) # Set for reproducibility
+for name, param in model.named_parameters():
+    param.data = torch.randn_like(param)
+print(model.num_parameters())  # 4122784
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+```
 ## Model Details