Text Generation
Transformers
Safetensors
Russian
English
qwen3
pretrained
causal-lm
bilingual
russian
english
text-generation-inference
Instructions to use domofon/Domofon-v1-0.8b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use domofon/Domofon-v1-0.8b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="domofon/Domofon-v1-0.8b-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("domofon/Domofon-v1-0.8b-base") model = AutoModelForCausalLM.from_pretrained("domofon/Domofon-v1-0.8b-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use domofon/Domofon-v1-0.8b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "domofon/Domofon-v1-0.8b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "domofon/Domofon-v1-0.8b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/domofon/Domofon-v1-0.8b-base
- SGLang
How to use domofon/Domofon-v1-0.8b-base 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 "domofon/Domofon-v1-0.8b-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "domofon/Domofon-v1-0.8b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "domofon/Domofon-v1-0.8b-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "domofon/Domofon-v1-0.8b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use domofon/Domofon-v1-0.8b-base with Docker Model Runner:
docker model run hf.co/domofon/Domofon-v1-0.8b-base
Domofon-v1-0.8b-base
A 0.9B parameter bilingual (Russian / English) base language model pretrained from scratch on a 660B token corpus.
Model Details
| Architecture | Qwen3 (dense decoder-only transformer) |
| Parameters | 0.9B (883M unique) |
| Hidden size | 1024 |
| Layers | 40 |
| Attention heads | 16 (8 KV heads, GQA) |
| Head dim | 64 |
| FFN dim | 4096 |
| Vocab size | 248,072 |
| Context length | 32,768 |
| Precision | float16 |
Training
- Pretrained from random initialization — no upstream weights were used
- Training corpus: 660B tokens, ~50/50 English and Russian
- Training framework: MaxText on Google Cloud TPU v5e-64
- This is a base model — no SFT, no chat tuning, no RLHF
Intended Use
This is a base pretrained model intended for research and as a foundation for downstream fine-tuning. It is not an instruction-following or chat model.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("domofon/Domofon-v1-0.8b-base")
tokenizer = AutoTokenizer.from_pretrained("domofon/Domofon-v1-0.8b-base")
inputs = tokenizer("Москва — столица", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Limitations
- Base model only — will not follow instructions or engage in dialogue without fine-tuning
- Training data mix is 50/50 EN/RU; performance on other languages is not evaluated
- No safety alignment has been applied
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