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

Libraries

How to use ViorikaAI-org/MicroLlama with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ViorikaAI-org/MicroLlama")
model = AutoModelForCausalLM.from_pretrained("ViorikaAI-org/MicroLlama")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ViorikaAI-org/MicroLlama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ViorikaAI-org/MicroLlama"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

MicroLlama / README.md

Dmitriy-Zemskov

Update README.md

25c8aec verified 5 days ago

preview code

raw

history blame contribute delete

1.73 kB

	---
	license: mit
	language:
	- ru
	tags:
	- llama
	- russian
	- conversational
	model-index:
	- name: MicroLLAMA
	results: []
	library_name: transformers
	model_creator: ViorikaAI
	pipeline_tag: text-generation
	---

	# 🦙 MicroLlama
	Эксперементальная микро-модель

	# Подробнее:

	## ⚙️ Детали модели
	- Архитектура: LLAMA
	- Параметры: 3M
	- Язык: Русский
	- Лицения: MIT

	## 🏋️ Детали Тренировки
	- Датасет: ``
	- Железо: ОДНА NVIDIA GEFORCE RTX 5060 TI (16GB VRAM)
	- Эпохи: - 18
	- СРЕДНИЙ LOSS: 0.4349
	- Оптимизатор: 5e-4
	- Контекст: 32 токена

	## 🏋️ Использование
	```python
	from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast

	model_name = "ViorikaAI-org/MicroLlama"
	tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	model.eval()

	prompt = "<s> Привет, как дела? =>"
	inputs = tokenizer(prompt, return_tensors="pt")

	outputs = model.generate(
	**inputs,
	max_new_tokens=32,
	temperature=0.7,
	top_k=50,
	top_p=0.9,
	do_sample=True,
	no_repeat_ngram_size=2,
	pad_token_id=tokenizer.pad_token_id,
	eos_token_id=tokenizer.eos_token_id
	)

	full_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
	answer = full_text.split("=>")[-1].replace("</s>", "").strip()

	print(f"Ответ модели: {answer}")
	```

	## 🛜 Наши Соц. Сети
	- Discord: https://discord.gg/8JwTv8zj8d , https://discord.gg/7JE7maH6cf
	- Telegram: https://t.me/viorika_official