| Property | Value |
|---|---|
| Parameters | 3 Billion |
| Context Length | 8,192 tokens |
| License | Apache 2.0 |
| Paper | BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model |
| Training Data | SlimPajama-627B |
What is btlm-3b-8k-base?
BTLM-3B-8k-base is a groundbreaking language model developed by Cerebras in partnership with Opentensor. This 3-billion parameter model achieves performance comparable to 7B models while requiring significantly fewer computational resources. It was trained on the Condor Galaxy 1 supercomputer using the SlimPajama-627B dataset.
Implementation Details
The model implements several cutting-edge architectural innovations including SwiGLU nonlinearity, ALiBi position embeddings, and maximal update parameterization (muP). Training was conducted in two phases: 75% with 2k sequence length and 25% with 8k sequence length, enabling robust long-sequence capabilities.
- Supports 8k context length through ALiBi position embeddings
- Can be quantized to 4-bit for deployment on devices with just 3GB memory
- Uses Byte Pair Encoding with a 50,257 token vocabulary
- Implements GPT-2 style architecture with modern enhancements
Core Capabilities
- Matches or exceeds performance of 7B parameter models
- Requires 71% fewer training FLOPs than comparable 7B models
- 58% smaller memory footprint for inference
- Excellent performance on tasks like MMLU (5-shot) and various 0-shot evaluations
- Effective context length extrapolation up to 10k tokens
Frequently Asked Questions
Q: What makes this model unique?
BTLM-3B-8k-base achieves 7B-level performance with just 3B parameters through innovative architecture choices and efficient training on high-quality data. It's also one of few 3B models supporting 8k sequence length.
Q: What are the recommended use cases?
The model is ideal for research into large language models, NLP applications, and ethics research. It's particularly well-suited for applications requiring long context windows and those with memory constraints. However, it should undergo additional safety testing before production deployment.