ajibawa-2023/OpenHermes-2.5-Code-290k-13B

OpenHermes-2.5-Code-290k-13B

OpenHermes-2.5-Code-290k-13B is a state of the art Llama-2 Fine-tune, which is trained on additional code dataset. This Model is much better than teknium's model. You can check the Eval results below. This model is trained on my existing dataset OpenHermes-2.5-Code-290k. This dataset is amalgamation of two datasets. I have used OpenHermes-2.5 a super quality dataset made avaliable by teknium. Other datset is my own Code-290k-ShareGPT. Dataset is in Vicuna/ShareGPT format. There are around 1.29 million set of conversations. I have cleaned the dataset provided by Teknium and removed metadata such as "source" & "category" etc. This dataset has primarily synthetically generated instruction and chat samples.

This model has enhanced coding capabilities besides other capabilities such as Blogging, story generation, Q&A and many more.

Training:

Entire model was trained on 4 x A100 80GB. For 2 epoch, training took 21 Days. Fschat & DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.

This is a full fine tuned model. Links for quantized models will be updated soon.

GPTQ, GGUF, AWQ & Exllama

GPTQ: TBA

GGUF: Link

AWQ: TBA

Exllama v2: Link

Special Thanks to LoneStriker and bartowski for quantising.

Example Prompt:

This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation. It can generate Story, Blogs .....

Context
You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

I will update soon.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.33
AI2 Reasoning Challenge (25-Shot)	57.34
HellaSwag (10-Shot)	80.48
MMLU (5-Shot)	56.53
TruthfulQA (0-shot)	52.50
Winogrande (5-shot)	74.82
GSM8k (5-shot)	58.30