Update README.md

README.md

@@ -2,8 +2,141 @@
 license: mit
 language:
 - en
+library_name: pytorch
+pipeline_tag: text-generation
+tags:
+- text-generation
+- causal-lm
+- language-model
+- base-model
+- pretrained
+- transformer
+- decoder-only
+- english
+- pytorch
+- sentencepiece
+- gqa
+- swiglu
+- rmsnorm
+- rope
+- 154m
+- qarvexium
+- qed
+- qed-base
+- qed-base-v3
+- foundation-model
+- foundation
+model-index:
+- name: QED-Base-v3
+  results: []
 ---
 
-A newly trained tokenizer will be used instead of recycling the old QED-Base-v1's tokenizer.
+# QED-Base-v3
 
-Please wait.
+QED-Base-v3 is a **~154M parameter causal language model** pretrained from scratch by **Qarvexium**. It is a base model — it has not been instruction-tuned or aligned for chat, and is designed to continue text rather than follow instructions or hold a conversation.
+
+## Model Details
+
+* **Developed by:** Qarvexium
+* **Model type:** Decoder-only causal language base model
+* **Language:** English
+* **License:** MIT
+* **Tokenizer:** QED-B3 tokenizer (SentencePiece, 56,000 vocabulary)
+
+### Architecture
+
+| Component             | Value                    |
+| --------------------- | ------------------------ |
+| Tokenizer             | QED-B3 tokenizer         |
+| Vocabulary size       | 56,000                   |
+| Model type            | Decoder-only Transformer |
+| Parameters            | ~154M                    |
+| Hidden size           | 768                      |
+| Layers                | 12                       |
+| Attention heads       | 12                       |
+| KV heads              | 4                        |
+| Attention             | GQA                      |
+| Intermediate FFN size | 1,792                    |
+| Activation            | SwiGLU                   |
+| Normalization         | RMSNorm                  |
+| Position encoding     | RoPE                     |
+| Context length        | 2,048                    |
+| RoPE theta            | 10,000                   |
+
+Weight-tied embeddings/LM head.
+
+## Uses
+
+### Direct Use
+
+As a base model, QED-Base-v3 is intended for:
+
+* Text completion / continuation
+* Research on small-scale language model pretraining
+* Experimenting with the QED architecture
+* Studying tokenizer and language-model behavior
+* A starting checkpoint for further fine-tuning
+* Instruction tuning and downstream model development
+
+### Out-of-Scope Use
+
+This model has not been instruction-tuned, RLHF'd, or safety-aligned.
+
+It should not be deployed directly as a chat or assistant model, or in applications requiring reliable instruction-following or content moderation, without additional fine-tuning and evaluation.
+
+## Bias, Risks, and Limitations
+
+QED-Base-v3 is an experimental base language model and may produce incorrect, nonsensical, repetitive, biased, or otherwise undesirable text.
+
+Because it is a base model, it does not have built-in instruction-following or refusal behavior.
+
+Its relatively small parameter count also means that its factual knowledge, reasoning ability, and generalization capabilities are limited compared with substantially larger language models.
+
+Outputs should be evaluated and filtered before use in user-facing applications.
+
+## How to Get Started
+
+The repository includes a lightweight inference implementation in `infer.py`.
+
+```python
+from infer import load_model, load_tokenizer, run
+
+model = load_model("QED-Base-v3.pt")
+tokenizer = load_tokenizer("qed-b3-tok.model")
+
+text = run(
+    "Once upon a time",
+    model,
+    tokenizer,
+    max_new_tokens=100
+)
+
+print(text)
+```
+
+For generation, the included inference implementation supports temperature, top-k, top-p, repetition penalty, and seeded generation.
+
+## Tokenizer
+
+QED-Base-v3 introduces a **new tokenizer trained specifically for this model generation**, rather than recycling the tokenizer used by QED-Base-v1.
+
+The tokenizer uses a **56,000-token vocabulary** and is provided in the repository as:
+
+`qed-b3-tok.model`
+
+## QED Family
+
+QED-Base-v3 is part of the QED family of language models.
+
+| Model              | Description                                          |
+| ------------------ | ---------------------------------------------------- |
+| QED-Base-v1        | First-generation QED base model                      |
+| QED-Base-v2        | Second-generation QED base model                     |
+| **QED-Base-v3**    | Third-generation QED base model with a new tokenizer |
+| QED-B1/B2-Instruction | Instruction-tuned QED variants                    |
+
+QED-Base-v3 is intended to serve as a foundation for future QED experiments and fine-tuned models.
+
+## License
+
+MIT License