Text Generation
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
text-generation-inference
Instructions to use archit11/worldmodel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use archit11/worldmodel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="archit11/worldmodel2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("archit11/worldmodel2") model = AutoModelForCausalLM.from_pretrained("archit11/worldmodel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use archit11/worldmodel2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "archit11/worldmodel2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "archit11/worldmodel2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/archit11/worldmodel2
- SGLang
How to use archit11/worldmodel2 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 "archit11/worldmodel2" \ --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": "archit11/worldmodel2", "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 "archit11/worldmodel2" \ --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": "archit11/worldmodel2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use archit11/worldmodel2 with Docker Model Runner:
docker model run hf.co/archit11/worldmodel2
| library_name: transformers | |
| license: mit | |
| base_model: nisten/Biggie-SmoLlm-0.15B-Base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: acha | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # acha | |
| This model is a fine-tuned version of [nisten/Biggie-SmoLlm-0.15B-Base](https://huggingface.co/nisten/Biggie-SmoLlm-0.15B-Base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.8727 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 5 | |
| - training_steps: 800 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 3.7094 | 0.0920 | 50 | 3.5122 | | |
| | 3.5464 | 0.1841 | 100 | 3.2981 | | |
| | 3.4292 | 0.2761 | 150 | 3.1894 | | |
| | 3.3576 | 0.3682 | 200 | 3.0917 | | |
| | 3.35 | 0.4602 | 250 | 3.0353 | | |
| | 3.3334 | 0.5522 | 300 | 2.9864 | | |
| | 3.3096 | 0.6443 | 350 | 2.9512 | | |
| | 3.2773 | 0.7363 | 400 | 2.9281 | | |
| | 3.2343 | 0.8283 | 450 | 2.9118 | | |
| | 3.2265 | 0.9204 | 500 | 2.9015 | | |
| | 3.0257 | 1.0124 | 550 | 2.8853 | | |
| | 2.9092 | 1.1045 | 600 | 2.8748 | | |
| | 2.9109 | 1.1965 | 650 | 2.8732 | | |
| | 2.9437 | 1.2885 | 700 | 2.8728 | | |
| | 2.894 | 1.3806 | 750 | 2.8727 | | |
| | 2.9286 | 1.4726 | 800 | 2.8727 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0 | |
| - Datasets 3.0.0 | |
| - Tokenizers 0.19.1 | |