Text Generation
Transformers
Safetensors
English
llama
causal-lm
pretrained
chytrej
base
tiny
text-generation-inference
Instructions to use pvlabs/Chytrej2-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pvlabs/Chytrej2-Mini with vLLM:
Install from pip and serve model
# 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 }'Use Docker
docker model run hf.co/pvlabs/Chytrej2-Mini
- SGLang
How to use pvlabs/Chytrej2-Mini 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 "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 }'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 "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 Model Runner
How to use pvlabs/Chytrej2-Mini with Docker Model Runner:
docker model run hf.co/pvlabs/Chytrej2-Mini
| language: | |
| - en | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| tags: | |
| - llama | |
| - causal-lm | |
| - pretrained | |
| - chytrej | |
| - base | |
| - tiny | |
| library_name: transformers | |
| datasets: | |
| - HuggingFaceFW/fineweb-edu | |
| # Chytrej2-Mini | |
| A fully custom pretrained language model built from scratch on the LLaMA architecture trained on 2B tokens of the [FineWeb Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) dataset. | |
| Built by [PingVortex Labs](https://github.com/PingVortexLabs). | |
| [](https://discord.gg/5SzkjVJBs2) | |
| --- | |
| ## Model Details | |
| + **Parameters:** 20M | |
| + **Context length:** 1024 tokens | |
| + **Language:** English only | |
| + **Format:** Base model | |
| + **Architecture:** LLaMA | |
| + **License:** Apache 2.0 | |
| --- | |
| ## Benchmark | |
| + The model achieves score of 35.77% on ARC-Easy benchmark. | |
| --- | |
| ## Usage | |
| ```python | |
| 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](https://pingvortex.com).* |