Pebble-10M-Chat

Pebble-10M-Chat is a compact, hybrid autoregressive chat 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: ~10,000,000 (10M)
• Hidden Dimension: 384
• Layers: 8 (6 Mamba2, 2 Attention)
• Vocab Size: 2,048 (Custom Byte-Level BPE)
• Context Length: 512
• Pretraining 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 base model was pretrained on a 25B token subset of the following datasets:

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

Pebble-10M-Chat was evaluated on several commonsense and arithmetic benchmarks.

Benchmark	Accuracy	Random Baseline
PIQA	51.41%	50.00%
ARC-Easy	26.09%	25.00%
ARC-Challenge	20.22%	25.00%
HellaSwag	25.30%	25.00%
ArithMark-2.0	27.28%	25.00%
ArithMark-3.0	27.50%	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.
• The model was additionally fine-tuned using supervised fine-tuning (SFT).

SFT Attribution

The 250,000,000 SFT tokens used for Pebble-10M-Chat were provided by smol-smoltalk.

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.

pip install transformers huggingface_hub torch
pip install causal-conv1d mamba-ssm

Here is a simple Python script to load the model and generate text interactively:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "basically-ai/Pebble-10M-Chat"

def main():
    print("Loading Pebble-10M-Chat...")
    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! 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