Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb-edu
+- HuggingFaceTB/finemath
+- HuggingFaceTB/smollm-corpus
+---
+
+![Banner](https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/0QdBKT5iKdrEy3iwLosAH.png)
+
+# DynamicMind-Mini
+
+DynamicMind-Mini is a decoder-only model trained on [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), [SmolLM-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath)
+
+The model has about 8.9M parameters and its based on [MiniBananaMind-v4-9M](https://huggingface.co/BananaMind/MiniBananaMind-v4-9M) architecture with a custom 8k-token byte-level BPE tokenizer with digit-aware tokenization.
+
+## 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 |
+| Base checkpoint | MiniBananaMind-v3-9M |
+| Continued pretraining checkpoint | Cosmopedia-v2 step 2,613 |
+
+## Tokenizer
+
+DynamicMind-Mini uses the same digit-aware 8k tokenizer as [MiniBananaMind-v4-9M](https://huggingface.co/BananaMind/MiniBananaMind-v4-9M).
+
+Digits are kept as separate tokens so numbers do not collapse into large number tokens during tokenization.
+
+Digit IDs:
+
+| Token | ID |
+|---|---:|
+| `1` | 9 |
+| `2` | 10 |
+| `3` | 11 |
+| `4` | 12 |
+| `5` | 13 |
+| `6` | 14 |
+| `7` | 15 |
+| `8` | 16 |
+| `9` | 17 |
+| `0` | 18 |
+
+## Usage
+
+This model uses custom architecture code, so load it with `trust_remote_code=True`.
+
+Install dependencies:
+
+```bash
+pip install -U transformers safetensors torch
+```
+
+Run inference:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "DedeProGames/DynamicMind-Mini"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
+).cuda().eval()
+
+prompt = "The meaning of life is "
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=64,
+        do_sample=False,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```