Chytrej
Collection
4 items • Updated
How to use pvlabs/Chytrej1-90M-Base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="pvlabs/Chytrej1-90M-Base") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pvlabs/Chytrej1-90M-Base")
model = AutoModelForCausalLM.from_pretrained("pvlabs/Chytrej1-90M-Base")How to use pvlabs/Chytrej1-90M-Base with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pvlabs/Chytrej1-90M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "pvlabs/Chytrej1-90M-Base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/pvlabs/Chytrej1-90M-Base
How to use pvlabs/Chytrej1-90M-Base with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "pvlabs/Chytrej1-90M-Base" \
--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/Chytrej1-90M-Base",
"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/Chytrej1-90M-Base" \
--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/Chytrej1-90M-Base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use pvlabs/Chytrej1-90M-Base with Docker Model Runner:
docker model run hf.co/pvlabs/Chytrej1-90M-Base
The first model in the Chytrej series. A fully custom pretrained language model built from scratch on the LLaMA architecture.
Chytrej (Czech slang for "clever/smart") is a long-term model series by PingVortex Labs. Every model in the series will be fully custom pretrained from scratch, then the model may be instruction fine-tuned on the custom base. The ongoing goal: every release must at least know the capital of France.
Built by PingVortex Labs.
Evaluated with lm-eval-harness, 0-shot:
| Task | Metric | Score |
|---|---|---|
| ARC-Easy | acc | 39.73% |
| ARC-Easy | acc_norm | 34.47% |
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
model = LlamaForCausalLM.from_pretrained("pvlabs/Chytrej1-90M-Base")
tokenizer = PreTrainedTokenizerFast.from_pretrained("pvlabs/Chytrej1-90M-Base")
# response: The capital of France is the city of Paris...
prompt = "The capital of France is"
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.