README.md

---
language:
  - en
license: apache-2.0
datasets:
  - hkust-nlp/deita-10k-v0
  - Felladrin/ChatML-deita-10k-v0
base_model: Felladrin/Minueza-32M-Base
pipeline_tag: text-generation
widget:
  - messages:
      - role: system
        content:
          You are a career counselor. The user will provide you with an individual
          looking for guidance in their professional life, and your task is to assist
          them in determining what careers they are most suited for based on their skills,
          interests, and experience. You should also conduct research into the various
          options available, explain the job market trends in different industries, and
          advice on which qualifications would be beneficial for pursuing particular fields.
      - role: user
        content: Heya!
      - role: assistant
        content: Hi! How may I help you?
      - role: user
        content:
          I am interested in developing a career in software engineering. What
          would you recommend me to do?
  - messages:
      - role: user
        content: Morning!
      - role: assistant
        content: Good morning! How can I help you today?
      - role: user
        content: Could you give me some tips for becoming a healthier person?
  - messages:
      - role: user
        content: Write the specs of a game about mages in a fantasy world.
  - messages:
      - role: user
        content: Tell me about the pros and cons of social media.
  - messages:
      - role: system
        content:
          You are a highly knowledgeable and friendly assistant. Your goal is to
          understand and respond to user inquiries with clarity. Your interactions are
          always respectful, helpful, and focused on delivering the most accurate information
          to the user.
      - role: user
        content: Hey! Got a question for you!
      - role: assistant
        content: Sure! What's it?
      - role: user
        content: What are some potential applications for quantum computing?
inference:
  parameters:
    max_new_tokens: 250
    do_sample: true
    temperature: 0.65
    top_p: 0.55
    top_k: 35
    repetition_penalty: 1.176
---

# Minueza-32M-Deita

- Base model: [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base)
- Dataset: [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-deita-10k-v0)] [hkust-nlp/deita-10k-v0](https://huggingface.co/datasets/hkust-nlp/deita-10k-v0)
- License: [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32M-Deita/resolve/main/license.txt)

## Recommended Prompt Format

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
```

## Recommended Inference Parameters

```yml
do_sample: true
temperature: 0.65
top_p: 0.55
top_k: 35
repetition_penalty: 1.176
```

## Usage Example

```python
from transformers import pipeline

generate = pipeline("text-generation", "Felladrin/Minueza-32M-Deita")

messages = [
    {
        "role": "system",
        "content": "You are a highly knowledgeable and friendly assistant. Your goal is to understand and respond to user inquiries with clarity. Your interactions are always respectful, helpful, and focused on delivering the most accurate information to the user.",
    },
    {
        "role": "user",
        "content": "Hey! Got a question for you!",
    },
    {
        "role": "assistant",
        "content": "Sure! What's it?",
    },
    {
        "role": "user",
        "content": "What are some potential applications for quantum computing?",
    },
]

prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output = generate(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.65,
    top_k=35,
    top_p=0.55,
    repetition_penalty=1.176,
)

print(output[0]["generated_text"])
```

## How it was trained

This model was trained with [SFTTrainer](https://huggingface.co/docs/trl/main/en/sft_trainer) using the following settings:

| Hyperparameter         | Value                                         |
| :--------------------- | :-------------------------------------------- |
| Epochs                 | 2                                             |
| Learning rate          | 2e-5                                          |
| Total train batch size | 16                                            |
| Max. sequence length   | 2048                                          |
| Weight decay           | 0                                             |
| Warmup ratio           | 0.1                                           |
| Optimizer              | Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| Scheduler              | cosine                                        |
| Seed                   | 42                                            |