Instructions to use MetaCore-LLM/MetaCore-1-Test-CPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

MetaCore-LLM commited on 15 days ago

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+---
+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
+---