Instructions to use ViorikaAI-org/MicroLlama-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ViorikaAI-org/MicroLlama-v3 with Transformers:

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

pipe = pipeline("text-generation", model="ViorikaAI-org/MicroLlama-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ViorikaAI-org/MicroLlama-v3")
model = AutoModelForCausalLM.from_pretrained("ViorikaAI-org/MicroLlama-v3", 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 ViorikaAI-org/MicroLlama-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ViorikaAI-org/MicroLlama-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ViorikaAI-org/MicroLlama-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ViorikaAI-org/MicroLlama-v3

SGLang

How to use ViorikaAI-org/MicroLlama-v3 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 "ViorikaAI-org/MicroLlama-v3" \
    --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": "ViorikaAI-org/MicroLlama-v3",
		"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 "ViorikaAI-org/MicroLlama-v3" \
        --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": "ViorikaAI-org/MicroLlama-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ViorikaAI-org/MicroLlama-v3 with Docker Model Runner:
```
docker model run hf.co/ViorikaAI-org/MicroLlama-v3
```

MicroLlama-v3

MicroLlama-v3 is a compact and ultra-fast 134M language model developed from scratch for text generation.

⚡ Specs

Architecture: Transformer / Causal LM (LLama)
Parameters: ~134M
Language: English, Russian
Format: ChatML
Context: 2048

📜 License

Distributed under the MIT License.

🇷🇺 Нажмите, чтобы открыть описание на русском языке (Click to expand Russian description)

MicroLlama-v3

MicroLlama-v3 — это компактная и сверхбыстрая 134М языковая модель, разработанная с нуля для генерации текста.

⚡ Характеристики

Архитектура: Transformer / Causal LM (LLama)
Объём параметров: ~134 млн
Основной язык: English, Russian
Формат диалога: ChatML
Контекст: 2048

📜 Лицензия

Распространяется под лицензией MIT.

🚀 Quick Start / Быстрый запуск

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ViorikaAI-org/MicroLlama-v3"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "<|im_start|>user\nПривет, как тебя зовут?<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty=1.25,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Downloads last month: 175

Safetensors

Model size

0.1B params

Tensor type

BF16

Collection including ViorikaAI-org/MicroLlama-v3

MicroLlama

Collection

Collection of MicroLlama models/Сборник моделей MicroLlama • 3 items • Updated 4 days ago • 1