Instructions to use yujiepan/llama-3-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yujiepan/llama-3-tiny-random with Transformers:

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

pipe = pipeline("text-generation", model="yujiepan/llama-3-tiny-random")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yujiepan/llama-3-tiny-random")
model = AutoModelForCausalLM.from_pretrained("yujiepan/llama-3-tiny-random")
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

vLLM

How to use yujiepan/llama-3-tiny-random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yujiepan/llama-3-tiny-random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujiepan/llama-3-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yujiepan/llama-3-tiny-random

SGLang

How to use yujiepan/llama-3-tiny-random 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 "yujiepan/llama-3-tiny-random" \
    --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": "yujiepan/llama-3-tiny-random",
		"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 "yujiepan/llama-3-tiny-random" \
        --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": "yujiepan/llama-3-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yujiepan/llama-3-tiny-random with Docker Model Runner:
```
docker model run hf.co/yujiepan/llama-3-tiny-random
```

llama-3-tiny-random / README.md

yujiepan

Update README.md

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	---
	library_name: transformers
	pipeline_tag: text-generation
	inference: true
	widget:
	- text: Hello!
	example_title: Hello world
	group: Python
	---

	This model is randomly initialized, using the config from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) but with smaller size.
	Note the model is in bfloat16.

	"yujiepan/llama-3-tiny-random" and "yujiepan/meta-llama-3-tiny-random" shares exactly the same files except the repo name.

	Codes:
	```python
	import transformers
	import torch
	import os
	from huggingface_hub import create_repo, upload_folder
	import accelerate

	source_model_id = 'meta-llama/Meta-Llama-3-8B-Instruct'
	save_path = '/tmp/yujiepan/meta-llama-3-tiny-random'
	repo_id = 'yujiepan/meta-llama-3-tiny-random'

	os.system(f'rm -rf {save_path}')

	config = transformers.AutoConfig.from_pretrained(
	source_model_id,
	trust_remote_code=True,
	)
	config._name_or_path = source_model_id
	config.hidden_size = 4
	config.intermediate_size = 14
	config.num_attention_heads = 2
	config.num_key_value_heads = 1
	config.num_hidden_layers = 2
	config.torch_dtype = "bfloat16"

	model = transformers.AutoModelForCausalLM.from_config(
	config,
	trust_remote_code=True,
	)

	with accelerate.init_empty_weights():
	model.generation_config = transformers.AutoModelForCausalLM.from_pretrained(source_model_id).generation_config

	model = model.to(torch.bfloat16)
	model.save_pretrained(save_path)

	tokenizer = transformers.AutoTokenizer.from_pretrained(
	source_model_id,
	trust_remote_code=True,
	)
	tokenizer.save_pretrained(save_path)

	model.float().generate(torch.tensor([[1, 2, 3]]).long(), max_length=16)

	os.system(f'ls -alh {save_path}')
	# os.system(f'rm -rf {save_path}/model.safetensors')
	create_repo(repo_id, exist_ok=True)
	upload_folder(repo_id='yujiepan/meta-llama-3-tiny-random', folder_path=save_path)
	upload_folder(repo_id='yujiepan/llama-3-tiny-random', folder_path=save_path)
	```