CodeLlama-34B-Instruct-GPTQ
| Property | Value |
|---|---|
| Model Size | 34B parameters |
| License | Llama 2 |
| Paper | Research Paper |
| Author | Meta (Original), TheBloke (Quantized) |
| Quantization | GPTQ (Multiple Options) |
What is CodeLlama-34B-Instruct-GPTQ?
CodeLlama-34B-Instruct-GPTQ is a quantized version of Meta's powerful code-focused language model, specifically optimized for instruction-following and code generation tasks. This GPTQ-quantized variant maintains the capabilities of the original model while reducing the computational requirements through various quantization options.
Implementation Details
The model offers multiple quantization configurations, including 4-bit and 8-bit options with different group sizes (32g, 64g, 128g) and Act Order settings. The implementation uses the AutoGPTQ framework and is compatible with modern GPU inference frameworks.
- Multiple GPTQ parameter options for different hardware configurations
- Optimized using Evol Instruct Code dataset for quantization
- Supports 4096 sequence length
- Compatible with ExLlama for 4-bit variants
Core Capabilities
- Code completion and generation
- Instruction-following for coding tasks
- Multi-language code understanding
- Optimized for production deployment
- Flexible quantization options for different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful capabilities of CodeLlama-34B with efficient quantization options, making it more accessible for deployment while maintaining high performance. The various quantization options allow users to balance between model quality and hardware requirements.
Q: What are the recommended use cases?
The model is ideal for code generation, code completion, and instruction-following tasks related to programming. It's particularly well-suited for production environments where efficient resource usage is crucial while maintaining high-quality code generation capabilities.
