Goliath 120B - Details

Goliath 120B is a large language model developed by Alpindale, an organization that combines two fine-tuned Llama 2 70B models into a single 120B parameter model. It operates under the Llama 2 Community License Agreement (LLAMA-2-CLA), allowing for flexible use while adhering to specific community guidelines. This model emphasizes enhanced performance through its merged architecture, offering a robust solution for complex language tasks.

Goliath 120B is an auto-regressive causal language model created by merging two fine-tuned Llama-2 70B models into a single 120B parameter architecture. It supports multiple quantized formats including GGUF, GPTQ, AWQ, and Exllamav2, enabling flexibility across different deployment scenarios. The model leverages a merge process combining Xwin and Euryale models with specific layer ranges to optimize performance. Its prompting format is compatible with Vicuna and Alpaca, with Vicuna expected to deliver the best results. This design emphasizes efficiency and adaptability for diverse language tasks.

Goliath 120B is a 120B parameter model, placing it in the Very Large Models (70B+) category, which means it is optimized for complex tasks but requires significant computational resources. Its 4k context length falls under Short Contexts (up to 4K Tokens), making it suitable for short to moderate-length tasks but less effective for very long texts. The combination of a large parameter count and moderate context length allows Goliath 120B to balance performance and efficiency, though it may struggle with extended sequences.

• Parameter Size: 120B (Very Large Models)
• Context Length: 4k (Short Contexts)

Goliath 120B is a large language model with 120B parameters that could enable possible uses in areas like text generation, language translation, and code generation. These applications are possible due to its high parameter count and flexibility in handling complex tasks, though further exploration is needed to confirm effectiveness. Possible uses might include creating detailed narratives, translating between languages with nuanced context, or generating code snippets for specific programming tasks. However, these possible uses require thorough testing to ensure alignment with specific requirements and constraints. The model’s design suggests it could support possible scenarios where high-quality output and adaptability are prioritized, but real-world performance would depend on factors like training data, deployment environment, and user needs.

• text generation
• language translation
• code generation

Goliath 120B is a large-scale language model with 120B parameters that could enable possible applications in areas like text generation, language translation, and code generation. These possible uses might include crafting detailed narratives, translating between languages with contextual accuracy, or generating code snippets for specific programming tasks. Possible applications could also extend to analyzing and summarizing large datasets or creating interactive dialogue systems, though these possible scenarios require further exploration. The model’s design suggests it could support possible uses where high-quality output and adaptability are prioritized, but each possible application must be thoroughly evaluated and tested before deployment to ensure alignment with specific goals and constraints.

• text generation
• language translation
• code generation

Goliath 120B’s medium q4 version requires multiple GPUs with at least 48GB VRAM (e.g., A100, RTX 4090/6000 series) and 32GB system memory for optimal performance, balancing precision and efficiency. This setup ensures the model can handle its 120B parameter scale while maintaining responsiveness. Users should verify their hardware meets these specifications to avoid performance bottlenecks.

• fp16, q2, q3, q4, q5, q6, q8

Goliath 120B is a large language model developed by Alpindale, combining two fine-tuned Llama 2 70B models into a single 120B parameter architecture under the Llama 2 Community License Agreement. It supports multiple quantized formats and is designed for complex tasks, emphasizing adaptability and performance across diverse applications.

Huggingface Model Page
Ollama Model Page