Mistral-22B-v0.2
| Property | Value |
|---|---|
| Parameter Count | 22.2B |
| Model Type | Dense Language Model |
| License | Apache 2.0 |
| Tensor Type | BF16 |
| Context Length | 32k tokens |
What is Mistral-22B-v0.2?
Mistral-22B-v0.2 is an innovative dense language model that represents a significant breakthrough in MOE compression technology. Created by Nicolas Mejia-Petit, this model successfully converts a mixture-of-experts architecture into a single dense 22B parameter model, trained on 8x more data than its predecessor.
Implementation Details
The model utilizes the Guanaco prompt format and incorporates various cutting-edge technologies including Unsloth AI for training optimization, leading to 2-3x speed increases and memory consumption reduction. It features a 32k sequence length and has been re-aligned to provide uncensored responses.
- Requires specific Guanaco chat template for optimal performance
- Implements BF16 tensor format for efficient computation
- Utilizes Flash Attention and QLora technologies
- Incorporates DPO datasets converted to SFT
Core Capabilities
- Advanced mathematical reasoning abilities
- Enhanced coding capabilities with practical implementation examples
- Multi-turn conversation handling
- JSON mode support and tool integration
- Agent-based task execution abilities
- 32k token context window
Frequently Asked Questions
Q: What makes this model unique?
This model represents the first successful MOE to Dense model conversion, maintaining the knowledge from multiple experts in a single 22B parameter model. It achieves this while delivering superior performance in areas like coding and mathematical reasoning.
Q: What are the recommended use cases?
The model excels in coding tasks, mathematical computations, multi-turn conversations, and agent-based tasks. It's particularly suited for applications requiring long context understanding and uncensored responses, though users should exercise appropriate caution with the latter capability.
