language:
- en
license: apache-2.0
model-index:
- name: flux-7b-v0.2
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: 66.55
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
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: 86.12
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
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: 65.38
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
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: 51.8
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
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: 79.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
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: 72.63
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chanwit/flux-7b-v0.2
name: Open LLM Leaderboard
Open Flux AI
Open Flux AI - Empowering developers with AI-driven Continuous Delivery solutions.
Welcome to Open Flux AI, a community initiative stemming from the Kube-7B project, dedicated to advancing AI expertise in Flux, Flagger, and Continuous Delivery technologies. Our mission is to use the power of AI to simplify and enhance the way developers interact with Flux and technologies around it.
Our first focus is on fine-tuning AI models to specialize in key areas such as Flux, Flagger, GitOps, and SOPS. By leveraging the raw data from Kube-7B and applying targeted Embedding techniques, we aim to create models that are highly proficient in these specific domains.
Our first major deliverable is flux-7b, a model based on Mistral 7B. flux-7b currently understands the basic knowledge of Flux, Flagger, GitOps, and SOPS.
flux-7b has demonstrated to be better than ChatGPT in these contexts. See the screenshot.
Getting Started
To begin using flux-7b, follow this simple command:
ollama run chanwit/flux-7b
The GGUF files of this model can be obtained from HuggingFace.
We are planning to delivery our models in other formats like Llamafiles and Docker Containers. Please stay tuned.
Models
flux-7b: Our first model, built on Mistral 7B, is designed to provide assistance in Flux, Flagger, GitOps, and SOPS.
Datasets
At the beginning, the Open Flux AI project shares its foundational dataset with the Kube-7B project but refines it to focus on specific areas. We continuously work on expanding our dataset, especially in areas like Flux commands and Custom Resources, to further enhance the model's capabilities.
Contributions
We welcome and greatly appreciate contributions, especially in the form of question and answer pairs. We are seeking contributions for new datasets centered around knowledge of Flux commands and CR generations.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.30 |
| AI2 Reasoning Challenge (25-Shot) | 66.55 |
| HellaSwag (10-Shot) | 86.12 |
| MMLU (5-Shot) | 65.38 |
| TruthfulQA (0-shot) | 51.80 |
| Winogrande (5-shot) | 79.32 |
| GSM8k (5-shot) | 72.63 |