Instructions to use demetera/llama-600M-rus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use demetera/llama-600M-rus with Transformers:

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

pipe = pipeline("text-generation", model="demetera/llama-600M-rus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("demetera/llama-600M-rus")
model = AutoModelForMultimodalLM.from_pretrained("demetera/llama-600M-rus")
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 demetera/llama-600M-rus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "demetera/llama-600M-rus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "demetera/llama-600M-rus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/demetera/llama-600M-rus

SGLang

How to use demetera/llama-600M-rus 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 "demetera/llama-600M-rus" \
    --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": "demetera/llama-600M-rus",
		"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 "demetera/llama-600M-rus" \
        --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": "demetera/llama-600M-rus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use demetera/llama-600M-rus with Docker Model Runner:
```
docker model run hf.co/demetera/llama-600M-rus
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama-600M-rus

Simple and customized amateur experimental model pretrained on the text fiction books from the scratch (updating the model regularly).
It could generate amateur, but more or less adequate output as well (in respect of training tokens).
The work can be used as a checkpoint for the further training or for experiments.

Simple usage example:

from transformers import LlamaTokenizerFast, LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained('demetera/llama-600M-rus')
tokenizer = LlamaTokenizerFast.from_pretrained('demetera/llama-600M-rus')

prompt = "Я вышел и улицу и"
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(inputs.input_ids, attention_mask = inputs.attention_mask, max_new_tokens=250, do_sample=True, top_k=50, top_p=0.95)

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

Downloads last month: 6

Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for demetera/llama-600M-rus

Quantizations

1 model