Instructions to use PrometheanStudio/styx-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrometheanStudio/styx-100m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PrometheanStudio/styx-100m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PrometheanStudio/styx-100m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PrometheanStudio/styx-100m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PrometheanStudio/styx-100m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PrometheanStudio/styx-100m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PrometheanStudio/styx-100m
- SGLang
How to use PrometheanStudio/styx-100m 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 "PrometheanStudio/styx-100m" \ --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": "PrometheanStudio/styx-100m", "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 "PrometheanStudio/styx-100m" \ --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": "PrometheanStudio/styx-100m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PrometheanStudio/styx-100m with Docker Model Runner:
docker model run hf.co/PrometheanStudio/styx-100m
Styx 100M
Styx 100M is a 96.5M-parameter causal language model developed by Promethean Studios as part of the Talos model family.
This release contains the model trained through 100,000 training steps.
Model Details
| Property | Value |
|---|---|
| Parameters | 96,482,304 |
| Vocabulary | 1,024 |
| Hidden size | 1,024 |
| Layers | 6 |
| Attention heads | 64 |
| KV heads | 32 |
| Feed-forward network | SwiGLU |
| Maximum sequence length | 512 |
| Training steps | 100,000 |
| Architecture | Causal Transformer |
Styx uses grouped-query attention with 32 KV heads and a SwiGLU feed-forward network.
Files
model.safetensors- model weights in SafeTensors formatconfig.json- model configurationtokenizer/tokenizer.json- Styx tokenizercheckpoints/step-100000.pt- original training checkpoint
The original .pt checkpoint preserves the training state and metadata, while model.safetensors contains the model weights only.
Training
The released checkpoint corresponds to:
100,000 training steps
The checkpoint was produced using the Talos training infrastructure developed by Promethean Studios.
Training metadata, including the training and validation loss recorded at the checkpoint, is preserved inside the original training checkpoint.
Intended Use
Styx 100M is primarily a research and development model.
It is intended for:
- experimentation with small language models
- local inference
- model architecture research
- benchmarking
- studying data and parameter efficiency
- development within the Talos ecosystem
This model should not be assumed to have the capabilities, reliability, or safety characteristics of larger production language models.
Limitations
Styx 100M is a relatively small language model and may produce:
- incorrect or fabricated information
- repetitive text
- incomplete responses
- poor reasoning
- inconsistent instruction following
- unsafe or undesirable generations
The model has not been presented as a general-purpose production assistant.
About Talos
Talos is the model development project of Promethean Studios, focused on building efficient language models at progressively larger scales.
Styx 100M represents the next stage of that development following the smaller Talos models.
License
Apache License 2.0.
Organization
Promethean Studios
Styx 100M is released as part of the Promethean Studios model family.
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