tomasmcm / docsgpt-7b-mistral

Source: Arc53/docsgpt-7b-mistral ✦ Quant: TheBloke/docsgpt-7B-mistral-AWQ ✦ DocsGPT is optimized for Documentation (RAG), fine-tuned for providing answers that are based on context

  • Public
  • 71 runs
  • Paper
  • License

Input

Output

Run time and cost

This model runs on Nvidia A40 GPU hardware. Predictions typically complete within 4 seconds. The predict time for this model varies significantly based on the inputs.

Readme

DocsGPT is optimized for Documentation (RAG optimised): Specifically fine-tuned for providing answers that are based on context, making it particularly useful for developers and technical support teams. We used the Lora fine tuning process. This model is fine tuned on top of zephyr-7b-beta

It’s an apache-2.0 license so you can use it for commercial purposes too.

Benchmarks:

Bacon: The BACON test is an internal assessment designed to evaluate the capabilities of neural networks in handling questions with substantial content. It focuses on testing the model’s understanding of context-driven queries, as well as its tendency for hallucination and attention span. The questions in both parts are carefully crafted, drawing from diverse sources such as scientific papers, complex code problems, and instructional prompts, providing a comprehensive test of the model’s ability to process and generate information in various domains. | Model | Score | |------------------------------|-------| | gpt-4 | 8.74 | | DocsGPT-7b-Mistral | 8.64 | | gpt-3.5-turbo | 8.42 | | zephyr-7b-beta | 8.37 | | neural-chat-7b-v3-1 | 7.88 | | Mistral-7B-Instruct-v0.1 | 7.44 | | openinstruct-mistral-7b | 5.86 | | llama-2-13b | 2.29 |

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MTbench with llm judge:

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#### First turn
Model Turn Score
gpt-4 1 8.956250
gpt-3.5-turbo 1 8.075000
DocsGPT-7b-Mistral 1 7.593750
zephyr-7b-beta 1 7.412500
vicuna-13b-v1.3 1 6.812500
alpaca-13b 1 4.975000
deepseek-coder-6.7b 1 4.506329
#### Second turn
Model Turn Score
gpt-4 2 9.025000
gpt-3.5-turbo 2 7.812500
DocsGPT-7b-Mistral 2 6.740000
zephyr-7b-beta 2 6.650000
vicuna-13b-v1.3 2 5.962500
deepseek-coder-6.7b 2 5.025641
alpaca-13b 2 4.087500
#### Average
Model Score
gpt-4 8.990625
gpt-3.5-turbo 7.943750
DocsGPT-7b-Mistral 7.166875
zephyr-7b-beta 7.031250
vicuna-13b-v1.3 6.387500
deepseek-coder-6.7b 4.764331
alpaca-13b 4.531250

To prepare your prompts make sure you keep this format:

### Instruction
(where the question goes)
### Context
(your document retrieval + system instructions)
### Answer