---
license: apache-2.0
base_model: Felladrin/Minueza-32M-Base
pipeline_tag: text-generation
language:
  - en
datasets:
  - HuggingFaceH4/ultrachat_200k
  - Felladrin/ChatML-ultrachat_200k
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: How I can become 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-UltraChat: A chat model with 32 million parameters

- Base model: [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base)
- Dataset: [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrachat_200k)] [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- License: [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32M-UltraChat/resolve/main/license.txt)
- Availability in other ML formats:
  - GGUF: [Felladrin/gguf-Minueza-32M-UltraChat](https://huggingface.co/Felladrin/gguf-Minueza-32M-UltraChat)
  - ONNX: [Felladrin/onnx-Minueza-32M-UltraChat](https://huggingface.co/Felladrin/onnx-Minueza-32M-UltraChat)

## 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-UltraChat")

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                                         |
| :--------------------- | :-------------------------------------------- |
| 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                                            |