Update README.md

README.md

@@ -1,3 +1,84 @@
 ---
 license: apache-2.0
+datasets:
+- HuggingFaceTB/smol-smoltalk
 ---
+
+![Banner](https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/0QdBKT5iKdrEy3iwLosAH.png)
+
+# DynamicMind-Mini-Instruct
+DynamicMind-Mini-Instruct is the instruction-tuned version of [DynamicMind-Mini](https://huggingface.co/DedeProGames/DynamicMind-Mini. It was fully fine-tuned on [HuggingFaceTB/smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) with loss applied only to assistant tokens and the assistant-ending EOS token.
+The model has about 8.9M, a **1,024-token context window**, and a custom **8,192-token digit-aware byte-level BPE tokenizer**. It supports system prompts, multi-turn conversations, and KV-cached generation.
+
+## Model Details
+
+| Field | Value |
+|---|---:|
+| Parameters | 8,884,992 |
+| Architecture | Custom Llama-style decoder |
+| Layers | 9 |
+| Hidden size | 256 |
+| Intermediate size | 768 |
+| Attention heads | 8 |
+| KV heads | 2 |
+| Vocabulary size | 8,192 |
+| Context length | 1,024 |
+| Embeddings | Tied input/output embeddings |
+| Weight format | safetensors |
+
+## Benchmarks
+
+![elo_curve_plot](https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/N4jnDc9E_4ovCtL7cD2TD.png)
+
+## Usage
+
+This model uses custom architecture code and must be loaded with `trust_remote_code=True`.
+
+```bash
+pip install -U transformers safetensors torch
+```
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "DedeProGames/DynamicMind-Mini-Instruct"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.bfloat16 if device == "cuda" and torch.cuda.is_bf16_supported() else torch.float32
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype=dtype,
+).to(device).eval()
+
+messages = [
+    {"role": "system", "content": "You are a concise and helpful assistant."},
+    {"role": "user", "content": "Hello!"},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+)
+inputs = {name: tensor.to(device) for name, tensor in inputs.items()}
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=192,
+        do_sample=False,
+        repetition_penalty=1.1,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        use_cache=True,
+    )
+
+new_tokens = output[0, inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+For multi-turn chat, append the generated assistant response and the next user message to `messages`, then render the chat template again.