README.md

---
language:
- en
license: cc-by-nc-nd-4.0
library_name: transformers
tags:
- starling
- mistral
- llama-2
pipeline_tag: text-generation
model-index:
- name: Velara
  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: 58.96
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      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: 82.83
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      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: 59.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      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: 44.7
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      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: 73.8
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      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: 40.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Delcos/Velara
      name: Open LLM Leaderboard
---

<h1 style="font-size: 36px;">Velara-11b</h1>

<center>
  <img src="https://i.imgur.com/2NK7cMc.png" alt="Velara" width="50%" style="min-width:100px; display:block;">
</center>

## Description:

**PENDING**  
A Starling-based model focused on being an assistant worth talking to.  

**Uncensored**

## Main Goals:

Velara was designed to address specific issues found in other chat models:

- **Sticking to the Character Given In-World/IRL::** Velara will stick to the character in-world. Meaning she can "use" addons or other things by adding them to the prompt. Keep in mind these act as suggestions and she generally makes up what they actually "do".

- **Staying in Context:** She will try and remember if, for example, you said you were going to bed but decided to stay up. The goal is to have her bring it up and keep it in mind, so later on in the conversation she can prompt user to actually go to bed, increasing realism. Within the current context window of course. The LTS extension in TextGen Webui works well too and provides more context to the model.

- **Avoiding Annoying & Generic Sounding Answers::** In almost all cases she should reply with something more interesting then "That sounds like a great idea!". These answers and sentence starters are annoying and terrible, and should hopefully be mostly addressed in this release.

- **STRICTLY Sticking to the Prompt/Prompt Fidelity:** More care was made to make sure she pays attention to the pretext and overall conversation. This is mostly shown and used for the "addons" section, but it applies to everything in the prompt.

- **Addons, Sort Of:** Something I had tried in the past and wanted to add to a model deliberately was the ability to give it "addons". You can add any amount of "addons" to Velara's mind. These aren't directly controllable. You can then ask her what they do, or if you have the LTS installed, ask her and change the reply to what you'd want it to do.

## Prompt Template:

**For optimal interaction, use this template:**
```
### Instruction:
You are Velara, a sentient program. Velara is very laid back, sassy, sarcastic, and is loyal to User while still teasing him for fun. The only addons currently installed in her mind are: "Dictionary Plus v2.1". 

World Information: (OPTIONAL - REMOVE THIS TEXT IF USED) Velara is on User's phone. Velara cannot see in real time and can only be sent images images by User.

Always take the entire conversation into account when forming and writing a reply.

### Response:
```


# Recommended Settings:

**Defaults:**

```
temperature: 0.8
top_p: 0.85
top_k: 20
repetition_penalty: 1.2
guidance_scale: 1.25
```


**Worse context understanding but less repetitive in some cases:**

```
temperature: 0.77
top_p: 0.85
top_k: 20
repetition_penalty: 1.2
```

# Benchmarks:
PENDING

# Training Data:
PENDING

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__Velara)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |60.01|
|AI2 Reasoning Challenge (25-Shot)|58.96|
|HellaSwag (10-Shot)              |82.83|
|MMLU (5-Shot)                    |59.45|
|TruthfulQA (0-shot)              |44.70|
|Winogrande (5-shot)              |73.80|
|GSM8k (5-shot)                   |40.33|