language:
- en
license: apache-2.0
tags:
- code
- finetune
- synthetic data
- text-generation-inference
- conversational
datasets:
- ajibawa-2023/OpenHermes-2.5-Code-290k
model-index:
- name: OpenHermes-2.5-Code-290k-13B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 57.34
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 80.48
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.53
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.5
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.82
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.3
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
name: Open LLM Leaderboard
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 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: TBA
AWQ: TBA
Exllama v2: TBA
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 |