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Rakesh44
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Model Details
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Rakesh44
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Input Modalities
Output Modalities
Supported Functionality
| Component | Value |
|---|---|
| Hidden size | 1280 |
| Layers | 24 |
| Attention heads | 16 |
| KV heads | 16 (no GQA — full multi-head attention) |
| Head dim | 80 |
| Intermediate (MLP) size | 3456 |
| Activation | SiLU |
| Norm | RMSNorm (eps 1e-5) |
| Positional encoding | RoPE (theta 10000) |
| Attention bias | None |
| Tied embeddings | Yes |
| Max position embeddings | 2048 |
Odyssey is a base language model pretrained on OpenWebText, so its native capability is fluent English text continuation rather than instruction following. Out of the box it is suited to simple language tasks: text completion, short-form generation, style and tone continuation, and serving as a foundation for further training.
Its main value is as a starting point for fine-tuning. Because it has already learned general English structure and web-scale language patterns, it can be adapted to focused downstream tasks with a relatively small labeled dataset. Suitable fine-tuning targets include:
Not suitable for high-stakes, factual-authority, or safety-critical use. As a small model, factual reliability and reasoning depth are limited.
python
from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained("Rakesh44/odyssey")model = AutoModelForCausalLM.from_pretrained("Rakesh44/odyssey")inputs = tokenizer("Once upon a time", return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Odyssey is a small model and may produce inaccurate, repetitive, or biased text reflecting its training data. Verify outputs before use. The 2048-token context and float32 weights mean memory use is higher than a quantized deployment would require; consider converting to bf16/fp16 for inference.