Update README.md

README.md

@@ -1,3 +1,155 @@
 ---
 license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- pebble
+- language-model
+- base-model
+- small-language-model
+- pytorch
+- safetensors
+- custom-code
+- mamba2
+- hybrid
+library_name: transformers
 ---
+
+# Pebble-25M
+
+![Banner](banner.png)
+
+Pebble-25M is a compact, hybrid autoregressive language model. It combines the efficiency of state-space models with the proven performance of attention layers, optimized using a custom Muon + AdamW optimizer split.
+
+## Model Details
+
+- **Architecture:** Hybrid Mamba2 / Transformer
+- **Block Pattern:** 3 Mamba2 blocks : 1 Attention block (repeating)
+- **Parameters:** ~24,500,000 (25M)
+- **Hidden Dimension:** 608
+- **Layers:** 8 (6 Mamba2, 2 Attention)
+- **Vocab Size:** 2,048 (Custom Byte-Level BPE)
+- **Context Length:** 2048
+- **Training Tokens:** ~25,000,000,000 (~25 Billion)
+- **Optimizer:** Muon (for 2D hidden weights) + AdamW (for embeddings, norms, and scalars)
+- **Precision:** fp32 master weights with bf16 autocast
+
+## Dataset Sources
+
+The model was trained on a 25B token subset of the following datasets (packaged as `Hoglet-33/25BT-PreTrain-v2`):
+
+| Dataset | Token Allocation | Share |
+|---|---:|---:|
+| FineWeb-Edu | 7.50 billion | 30% |
+| DCLM | 5.00 billion | 20% |
+| Cosmopedia-v2 | 3.75 billion | 15% |
+| FineMath-4+ | 3.75 billion | 15% |
+| FinePhrase | 3.00 billion | 12% |
+| NPset | 2.00 billion | 8% |
+
+## Benchmarks
+
+| Benchmark | **Pebble-25M** | Pebble-25M Chat | Pebble-10M | BananaMind-2-Mini | Random |
+|---|---:|---:|---:|---:|---:|
+| PIQA | 59.25% | 53.37% | 58.43% | **59.63%** | 50.00% |
+| ARC-Easy | 38.17% | 26.68% | 37.29% | **39.86%** | 25.00% |
+| ARC-Challenge | 18.60% | 19.62% | 18.60% | **25.68%** | 25.00% |
+| HellaSwag | 27.62% | 25.63% | 26.81% | **29.72%** | 25.00% |
+| ArithMark-2.0 | 27.60% | 26.20% | **27.64%** | 27.52% | 25.00% |
+| ArithMark-3.0 | 33.80% | 28.80% | 32.80% | **34.90%** | 25.00% |
+
+### Evaluation Notes
+
+- PIQA, ARC-Easy, ARC-Challenge, and HellaSwag were evaluated on their respective test splits.
+- ArithMark-2.0 was evaluated on its train split due to the lack of a suitable test split.
+- ArithMark-3.0 was evaluated on its train split due to the lack of a suitable test split.
+- Results were obtained using zero-shot multiple-choice evaluation.
+- No task-specific fine-tuning was performed.
+
+## Usage
+
+To run the model for text generation, you will need to install the required dependencies. The included Mamba2 implementation relies on CUDA/Triton kernels and is intended to run on a CUDA-enabled GPU. Ampere-class GPUs or newer are recommended.
+
+> **Note:** The model uses custom architecture code, so you must pass `trust_remote_code=True` when loading both the tokenizer and the model.
+
+### Installation
+
+```bash
+pip install transformers huggingface_hub torch
+pip install causal-conv1d mamba-ssm  
+```
+
+### Generation
+
+Here is a simple Python script to load the model and generate text interactively:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "basically-ai/Pebble-25M"
+
+
+def main():
+    print("Loading Pebble 25M...")
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+    )
+
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+        dtype=torch.float32,
+    ).to("cuda")
+
+    model.eval()
+
+    print(
+        f"Model loaded successfully! "
+        f"VRAM usage: {torch.cuda.memory_allocated() / 1e9:.2f} GB"
+    )
+    print("Type 'quit' or 'exit' to stop.\n")
+
+    while True:
+        prompt = input("You: ")
+
+        if prompt.lower() in ["quit", "exit"]:
+            break
+
+        # Tokenize the prompt
+        inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+        # Generate text
+        print("Pebble: ", end="", flush=True)
+
+        with torch.inference_mode():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=100,       # How many tokens to generate
+                do_sample=True,           # Use sampling (more creative)
+                temperature=0.7,          # Controls randomness
+                top_k=50,                 # Consider top 50 tokens
+                top_p=0.95,               # Nucleus sampling
+                repetition_penalty=1.2,   # Prevent repeating words
+            )
+
+        # Decode and print (skip the prompt part)
+        generated_text = tokenizer.decode(
+            outputs[0][inputs["input_ids"].shape[1]:],
+            skip_special_tokens=True,
+        )
+
+        print(generated_text)
+        print()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+## License
+
+Apache 2.0