Text Generation
Transformers
Safetensors
Russian
llama
russian
causal-lm
continued-pretraining
cpt
tiny
text-generation-inference
Instructions to use MetaCore-LLM/MetaCore-1-Test-CPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MetaCore-LLM/MetaCore-1-Test-CPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MetaCore-LLM/MetaCore-1-Test-CPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MetaCore-LLM/MetaCore-1-Test-CPT") model = AutoModelForCausalLM.from_pretrained("MetaCore-LLM/MetaCore-1-Test-CPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MetaCore-LLM/MetaCore-1-Test-CPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MetaCore-LLM/MetaCore-1-Test-CPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaCore-LLM/MetaCore-1-Test-CPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MetaCore-LLM/MetaCore-1-Test-CPT
- SGLang
How to use MetaCore-LLM/MetaCore-1-Test-CPT 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 "MetaCore-LLM/MetaCore-1-Test-CPT" \ --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": "MetaCore-LLM/MetaCore-1-Test-CPT", "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 "MetaCore-LLM/MetaCore-1-Test-CPT" \ --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": "MetaCore-LLM/MetaCore-1-Test-CPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MetaCore-LLM/MetaCore-1-Test-CPT with Docker Model Runner:
docker model run hf.co/MetaCore-LLM/MetaCore-1-Test-CPT
| language: | |
| - ru | |
| license: mit | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - russian | |
| - llama | |
| - causal-lm | |
| - continued-pretraining | |
| - cpt | |
| - tiny | |
| base_model: MetaCore-LLM/MetaCore-1-Test-Base | |
| model-index: | |
| - name: MetaCore-1-Test-CPT | |
| results: [] | |
| # MetaCore-1-Test-CPT | |
| **MetaCore-1-Test-CPT** is a lightweight Russian language model obtained by **continued pre‑training (CPT)** the base model [`MetaCore-1-Test-Base`](https://huggingface.co/MetaCore-LLM/MetaCore-1-Test-Base) on a broader and more diverse Russian text corpus. | |
| This model serves as an intermediate checkpoint, offering improved language understanding over the base model, and is intended to be further fine‑tuned for specific downstream tasks (e.g., instruction tuning, classification). | |
| - **Developer:** MetaCore-LLM | |
| - **Architecture:** Custom LLaMA-style (tiny config) | |
| - **Language:** Russian | |
| - **Parameter count:** ~16.2 million | |
| --- |