Instructions to use DLMveloper/Solade_1.0Demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DLMveloper/Solade_1.0Demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DLMveloper/Solade_1.0Demo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DLMveloper/Solade_1.0Demo") model = AutoModelForCausalLM.from_pretrained("DLMveloper/Solade_1.0Demo") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DLMveloper/Solade_1.0Demo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DLMveloper/Solade_1.0Demo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DLMveloper/Solade_1.0Demo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DLMveloper/Solade_1.0Demo
- SGLang
How to use DLMveloper/Solade_1.0Demo 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 "DLMveloper/Solade_1.0Demo" \ --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": "DLMveloper/Solade_1.0Demo", "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 "DLMveloper/Solade_1.0Demo" \ --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": "DLMveloper/Solade_1.0Demo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DLMveloper/Solade_1.0Demo with Docker Model Runner:
docker model run hf.co/DLMveloper/Solade_1.0Demo
Model Card for Solade
Model Details
Model Description
Языковая модель на 1.2 миллиардов параметр,
- Developed by: DLMveloper
- Model type: Decoder-only transformer (text generation)
- Language(s): Russian, English, Kazakh
- License: [The license is being examined by lawyers]
Model Sources
- Repository: https://huggingface.co/DLMveloper/Solade (Testing)
Uses
Direct Use
Генерация текста на русском, английском, казахском языках.
Out-of-Scope Use
Модель обучена на ограниченном объёме данных (300 шагов), не предназначена для высокоточных или критичных задач.
Bias, Risks, and Limitations
Модель обучена на небольшом количестве шагов и может выдавать несвязный или некорректный текст.
How to Get Started with the Model
Training Details
Training Data
Датасет: DLMveloper/DLM_DataSet (подвыборка ~20000 примеров)
Training Procedure
Training Hyperparameters
- Training regime: ???????
- Steps: ???
- Batch size: ?
- Learning rate: ?????
- Sequence length: ???.
Speeds, Sizes, Times
- Размер модели: ????? (??-bit quantized)
Technical Specifications
Model Architecture and Objective
- Параметров: ??
- Слоёв: ??
- Hidden size: ????
- Attention heads: ??
- Intermediate size (FFN): ????
- Vocab size: ???
- Компоненты: ???????
Compute Infrastructure
Software
???????????
- Downloads last month
- 89
Model tree for DLMveloper/Solade_1.0Demo
Dataset used to train DLMveloper/Solade_1.0Demo
Viewer • Updated • 3.6M • 497 • 1