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---
license: apache-2.0
base_model: Felladrin/Minueza-32M-Base
pipeline_tag: text-generation
language:
- en
datasets:
- databricks/databricks-dolly-15k
- Felladrin/ChatML-databricks-dolly-15k
- euclaise/reddit-instruct-curated
- Felladrin/ChatML-reddit-instruct-curated
- THUDM/webglm-qa
- Felladrin/ChatML-WebGLM-QA
- starfishmedical/webGPT_x_dolly
- Felladrin/ChatML-webGPT_x_dolly
- LDJnr/Capybara
- Felladrin/ChatML-Capybara
- Open-Orca/SlimOrca-Dedup
- Felladrin/ChatML-SlimOrca-Dedup
- HuggingFaceH4/ultrachat_200k
- Felladrin/ChatML-ultrachat_200k
- nvidia/HelpSteer
- Felladrin/ChatML-HelpSteer
- sablo/oasst2_curated
- Felladrin/ChatML-oasst2_curated
- CohereForAI/aya_dataset
- Felladrin/ChatML-aya_dataset
- argilla/distilabel-capybara-dpo-7k-binarized
- Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized
- argilla/distilabel-intel-orca-dpo-pairs
- Felladrin/ChatML-distilabel-intel-orca-dpo-pairs
- argilla/ultrafeedback-binarized-preferences
- Felladrin/ChatML-ultrafeedback-binarized-preferences
- sablo/oasst2_dpo_pairs_en
- Felladrin/ChatML-oasst2_dpo_pairs_en
- NeuralNovel/Neural-DPO
- Felladrin/ChatML-Neural-DPO
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: system
content: You are a highly knowledgeable assistant. Help the user as much as you can.
- role: user
content: How can I become a healthier person?
- messages:
- role: system
content: You are a helpful assistant who gives creative responses.
- role: user
content: Write the specs of a game about mages in a fantasy world.
- messages:
- role: system
content: You are a helpful assistant who answers user's questions with details.
- role: user
content: Tell me about the pros and cons of social media.
- messages:
- role: system
content: You are a helpful assistant who answers user's questions with details and curiosity.
- 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-Chat: A chat model with 32 million parameters
- Base model: [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base)
- Datasets used during SFT:
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-databricks-dolly-15k)] [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-reddit-instruct-curated)] [euclaise/reddit-instruct-curated](https://huggingface.co/datasets/euclaise/reddit-instruct-curated)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-WebGLM-QA)] [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-webGPT_x_dolly)] [starfishmedical/webGPT_x_dolly](https://huggingface.co/datasets/starfishmedical/webGPT_x_dolly)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Capybara)] [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-SlimOrca-Dedup)] [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrachat_200k)] [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-HelpSteer)] [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_curated)] [sablo/oasst2_curated](https://huggingface.co/datasets/sablo/oasst2_curated)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-aya_dataset)] [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset)
- Datasets used during DPO:
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized)] [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-intel-orca-dpo-pairs)] [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrafeedback-binarized-preferences)] [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_dpo_pairs_en)] [sablo/oasst2_dpo_pairs_en](https://huggingface.co/datasets/sablo/oasst2_dpo_pairs_en)
- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Neural-DPO)] [NeuralNovel/Neural-DPO](https://huggingface.co/datasets/NeuralNovel/Neural-DPO)
- License: [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32M-Chat/resolve/main/license.txt)
- Availability in other ML formats:
- GGUF: [Felladrin/gguf-Minueza-32M-Chat](https://huggingface.co/Felladrin/gguf-Minueza-32M-Chat)
- ONNX: [Felladrin/onnx-Minueza-32M-Chat](https://huggingface.co/Felladrin/onnx-Minueza-32M-Chat)
## 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-Chat")
messages = [
{
"role": "system",
"content": "You are a helpful assistant who answers the user's questions with details and curiosity.",
},
{
"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 [SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer) and [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer), in several sessions, using the following settings:
For Supervised Fine-Tuning:
| Hyperparameter | Value |
| :-------------------------- | :-------------------------------------------- |
| learning_rate | 2e-5 |
| total_train_batch_size | 24 |
| max_seq_length | 2048 |
| weight_decay | 0 |
| warmup_ratio | 0.02 |
For Direct Preference Optimization:
| Hyperparameter | Value |
| :-------------------------- | :-------------------------------------------- |
| learning_rate | 7.5e-7 |
| total_train_batch_size | 6 |
| max_length | 2048 |
| max_prompt_length | 1536 |
| max_steps | 200 |
| weight_decay | 0 |
| warmup_ratio | 0.02 |
| beta | 0.1 |
|