Instructions to use aeonium/Aeonium-v1.1-Chat-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aeonium/Aeonium-v1.1-Chat-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aeonium/Aeonium-v1.1-Chat-4B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1.1-Chat-4B") model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1.1-Chat-4B") 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 aeonium/Aeonium-v1.1-Chat-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aeonium/Aeonium-v1.1-Chat-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aeonium/Aeonium-v1.1-Chat-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aeonium/Aeonium-v1.1-Chat-4B
- SGLang
How to use aeonium/Aeonium-v1.1-Chat-4B 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 "aeonium/Aeonium-v1.1-Chat-4B" \ --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": "aeonium/Aeonium-v1.1-Chat-4B", "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 "aeonium/Aeonium-v1.1-Chat-4B" \ --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": "aeonium/Aeonium-v1.1-Chat-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use aeonium/Aeonium-v1.1-Chat-4B with Docker Model Runner:
docker model run hf.co/aeonium/Aeonium-v1.1-Chat-4B
Aeoinum v1.1 Chat 4B
A state-of-the-art language model for Russian language processing. The model is fine-tuned for dialogues, SFT only. Trained on 2xNVIDIA L40S
Usage
Example for running a model on NVIDIA CUDA:
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "aeonium/Aeonium-v1.1-Chat-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=torch.bfloat16,
)
chat = [
{ "role": "user", "content": "ะัะธะฒะตั!" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=48)
print(tokenizer.decode(outputs[0]))
Content Warning
Aeonium v1.1 is a large language model trained on a broad dataset from the internet. As such, it may generate text that contains biases, offensive language, or other disapproving content. The model outputs should not be considered factual or representative of any individual's beliefs or identity. Users should exercise caution and apply careful filtering when using Aeonium's generated text, especially for sensitive or high-stakes applications. The developers do not condone generating harmful, biased, or unethical content.
Copyright
The model is released under the Apache 2.0 license.
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