HuggingFaceFW/fineweb-edu
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How to use pvlabs/Chytrej2-Mini with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="pvlabs/Chytrej2-Mini") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pvlabs/Chytrej2-Mini")
model = AutoModelForCausalLM.from_pretrained("pvlabs/Chytrej2-Mini")How to use pvlabs/Chytrej2-Mini with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pvlabs/Chytrej2-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "pvlabs/Chytrej2-Mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/pvlabs/Chytrej2-Mini
How to use pvlabs/Chytrej2-Mini with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "pvlabs/Chytrej2-Mini" \
--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": "pvlabs/Chytrej2-Mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "pvlabs/Chytrej2-Mini" \
--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": "pvlabs/Chytrej2-Mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use pvlabs/Chytrej2-Mini with Docker Model Runner:
docker model run hf.co/pvlabs/Chytrej2-Mini
A fully custom pretrained language model built from scratch on the LLaMA architecture trained on 2B tokens of the FineWeb Edu dataset.
Built by PingVortex Labs.
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
model = LlamaForCausalLM.from_pretrained("pvlabs/Chytrej2-Mini")
tokenizer = PreTrainedTokenizerFast.from_pretrained("pvlabs/Chytrej2-Mini")
prompt = "Neural Networks are"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, repetition_penalty=1.3)
print(tokenizer.decode(outputs[0]))
Made by PingVortex.