Instructions to use ctu-aic/Llama-3.1-8B_cp-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ctu-aic/Llama-3.1-8B_cp-mix with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ctu-aic/Llama-3.1-8B_cp-mix")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ctu-aic/Llama-3.1-8B_cp-mix")
model = AutoModelForCausalLM.from_pretrained("ctu-aic/Llama-3.1-8B_cp-mix", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ctu-aic/Llama-3.1-8B_cp-mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ctu-aic/Llama-3.1-8B_cp-mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ctu-aic/Llama-3.1-8B_cp-mix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ctu-aic/Llama-3.1-8B_cp-mix

SGLang

How to use ctu-aic/Llama-3.1-8B_cp-mix 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 "ctu-aic/Llama-3.1-8B_cp-mix" \
    --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": "ctu-aic/Llama-3.1-8B_cp-mix",
		"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 "ctu-aic/Llama-3.1-8B_cp-mix" \
        --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": "ctu-aic/Llama-3.1-8B_cp-mix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ctu-aic/Llama-3.1-8B_cp-mix with Docker Model Runner:
```
docker model run hf.co/ctu-aic/Llama-3.1-8B_cp-mix
```

mlynatom commited on May 17, 2025

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@@ -25,7 +25,7 @@ tags:
 ---
 # Model Card for Llama 3.1 8B -> CP_(mix)
 <!-- Provide a quick summary of what the model is/does. -->
-Llama 3.1 8B continuously pretrained on the Czech subset of FineWeb2. **More information in the thesis**: TBA. (The notation is thesis is: B->CP_(cs+en))
 <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />

 ---
 # Model Card for Llama 3.1 8B -> CP_(mix)
 <!-- Provide a quick summary of what the model is/does. -->
+Llama 3.1 8B continuously pretrained on a mixture of FineWeb2 and FineWeb-Edu datasets. **More information in the thesis**: TBA. (The notation is thesis is: B->CP_(cs+en))
 <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />