# Supported Models

## Supported LLMs

The following models are currently supported for fine-tuning:

| Model                                  | Parameters | Size      | Architecture       | Context Window |
| -------------------------------------- | ---------- | --------- | ------------------ | -------------- |
| meta-llama/Meta-Llama-3.1-8B           | 8.03B      | 16.07 GB  | LlamaForCausalLM   | 32,768 tokens  |
| meta-llama/Meta-Llama-3.1-8B-Instruct  | 8.03B      | 16.07 GB  | LlamaForCausalLM   | 32,768 tokens  |
| meta-llama/Meta-Llama-3.1-70B          | 70.6B      | 141.1 GB  | LlamaForCausalLM   | 32,768 tokens  |
| meta-llama/Meta-Llama-3.1-70B-Instruct | 70.6B      | 141.1 GB  | LlamaForCausalLM   | 32,768 tokens  |
| meta-llama/Llama-2-7b-hf               | 6.74B      | 13.49 GB  | LlamaForCausalLM   | 4,096 tokens   |
| meta-llama/Llama-2-7b-chat-hf          | 6.74B      | 13.49 GB  | LlamaForCausalLM   | 4,096 tokens   |
| meta-llama/Llama-2-13b-hf              | 13B        | 26.03 GB  | LlamaForCausalLM   | 4,096 tokens   |
| meta-llama/Llama-2-13b-chat-hf         | 13B        | 26.03 GB  | LlamaForCausalLM   | 4,096 tokens   |
| meta-llama/Llama-2-70b-hf              | 69B        | 137.96 GB | LlamaForCausalLM   | 4,096 tokens   |
| meta-llama/Llama-2-70b-chat-hf         | 69B        | 137.96 GB | LlamaForCausalLM   | 4,096 tokens   |
| mistralai/Mistral-7B-v0.1              | 7.24B      | 14.48 GB  | MistralForCausalLM | 32,768 tokens  |
| mistralai/Mistral-7B-v0.3              | 7.25B      | 14.5 GB   | MistralForCausalLM | 32,768 tokens  |
| mistralai/Mistral-7B-Instruct-v0.1     | 7.24B      | 14.48 GB  | MistralForCausalLM | 32,768 tokens  |
| mistralai/Mistral-7B-Instruct-v0.2     | 7.24B      | 14.48 GB  | MistralForCausalLM | 32,768 tokens  |
| mistralai/Mistral-7B-Instruct-v0.3     | 7.25B      | 14.5 GB   | MistralForCausalLM | 32,768 tokens  |
| phi-3-mini-4k-instruct                 | 3.82B      | 7.64GB    | Phi3ForCausalLM    | 4,096 tokens   |
| google/gemma-2b                        | 2.51B      | 5.01 GB   | GemmaForCausalLM   | 8,192 tokens   |
| google/gemma-1.1-2b-it                 | 2.51B      | 5.01 GB   | GemmaForCausalLM   | 8,192 tokens   |
| google/gemma-7b                        | 8.54B      | 17.08 GB  | GemmaForCausalLM   | 8,192 tokens   |
| google/gemma-1.1-7b-it                 | 8.54B      | 17.08 GB  | GemmaForCausalLM   | 8,192 tokens   |
| google/gemma-2-9b                      | 9.24B      | 18.48 GB  | Gemma2ForCausalLM  | 8,192 tokens   |
| google/gemma-2-9b-it                   | 9.24B      | 18.48 GB  | Gemma2ForCausalLM  | 8,192 tokens   |
| google/gemma-2-27b                     | 27.2B      | 54.45 GB  | Gemma2ForCausalLM  | 8,192 tokens   |
| google/gemma-2-27b-it                  | 27.2B      | 54.45 GB  | Gemma2ForCausalLM  | 8,192 tokens   |
| Qwen/Qwen2-7B                          | 7.62B      | 15.23 GB  | Qwen2ForCausalLM   | 32,768 tokens  |
| Qwen/Qwen2-7B-Instruct                 | 7.62B      | 15.23 GB  | Qwen2ForCausalLM   | 32,768 tokens  |
| Qwen/Qwen2-72B                         | 72.7B      | 145.41 GB | Qwen2ForCausalLM   | 8,192 tokens   |
| Qwen/Qwen2-72B-Instruct                | 72.7B      | 145.41 GB | Qwen2ForCausalLM   | 8,192 tokens   |

## LLM Types

As you can see from the table above, there are two types of LLMs available:

1. **Base Models**: These are foundational models trained on large, diverse datasets without specific task instructions. The main objective of such models is text completion.
2. **Instruct Models** (usually with `base`, `instruct`, `chat` or `it` present in their name): These models are fine-tuned from base models to excel in interactive or instruction-based tasks. They are often trained on structured dialogues, enabling them to adopt specific roles or tones (e.g., "helpful assistant") and they are specialized for interpreting and responding to prompts in a conversational, helpful manner.

Currently, only fine-tuning for instruct models is supported. Base model fine-tuning for text completion will be supported in the future.


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