Update README.md

README.md

@@ -1,5 +1,87 @@
 ---
+language:
+- en
 license: mit
+library_name: pytorch
+tags:
+- text-generation
+- causal-lm
+- pretrained
+- qarvexium
+pipeline_tag: text-generation
 ---
 
-Prepearing for release
+# QED-Base-v2
+
+QED-Base-v2 is a ~107M parameter causal language model pretrained from scratch by [Qarvexium](https://huggingface.co/Qarvexium). It is a **base model** — it has not been instruction-tuned or aligned for chat, and will 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-B1 tokenizer (SentencePiece BPE, 48,000 vocabulary)
+
+### Architecture
+
+| Component | Value |
+| --------------------- | -----------------------: |
+| Tokenizer             |     QED-B1 tokenizer |
+| Vocabulary size       |               48,000 |
+| Model type            | Decoder-only Transformer |
+| Parameters            |                    ~107M |
+| Hidden size           |                      768 |
+| Layers                |                       12 |
+| Attention heads       |                       12 |
+| KV heads              |                        4 |
+| Attention             |                      GQA |
+| Intermediate FFN size |                     1792 |
+| Activation            |                   SwiGLU |
+| Normalization         |                  RMSNorm |
+| Position encoding     |                     RoPE |
+| Context length        |                     2048 |
+| RoPE theta            |                    10000 |
+| RMS epsilon           |                     1e-6 |
+
+Weight-tied embeddings/LM head.
+
+## Uses
+
+### Direct Use
+
+As a base model, QED-Base-v2 is intended for:
+- Text completion / continuation
+- Research on small-scale language model pretraining
+- A starting checkpoint for further fine-tuning (instruction tuning, domain adaptation, etc.)
+
+### Out-of-Scope Use
+
+This model has **not** been instruction-tuned, RLHF'd, or safety-aligned. It should not be deployed directly in a chat or assistant product, or in any application where reliable instruction-following or content moderation is required, without further fine-tuning and evaluation.
+
+## Bias, Risks, and Limitations
+
+QED-Base-v2 was trained on a large web-crawled corpus and will reflect the biases, inaccuracies, and occasionally toxic content present in that data. As a base model it has no built-in refusal behavior or safety tuning — outputs should be filtered/evaluated before use in any user-facing setting. At ~107M parameters, factual accuracy and reasoning ability are limited compared to larger models.
+
+## How to Get Started
+
+```python
+from infer import load_model, load_tokenizer, run
+
+model = load_model("QED-Base-v2.pt")
+tokenizer = load_tokenizer("tok.model")
+
+text = run("Once upon a time", model, tokenizer, max_new_tokens=100)
+print(text)
+```
+
+## Citation
+
+```bibtex
+@misc{qed-base-v2,
+  title  = {QED-Base-v2},
+  author = {Qarvexium},
+  year   = {2026},
+  url    = {https://huggingface.co/Qarvexium}
+}
+```