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EN
Minueza 32M Chat
Minueza-32M-Chat is a chat model with 32 million parameters, based on Felladrin/Minueza-32M-Base and trained with supervised fine-tuning (SFT) and direct preference optimization (DPO).
Downloads 77
Release Time : 2/25/2024
Model Overview
This is a small yet efficient chat model suitable for various dialogue scenarios, capable of providing helpful responses and suggestions.
Model Features
Compact and Efficient
With only 32 million parameters, it achieves decent dialogue capabilities through meticulous training
Multi-dataset Training
Trained using multiple high-quality datasets including Dolly, WebGLM, Capybara, etc.
Direct Preference Optimization
Utilizes DPO training method to optimize response quality
Model Capabilities
Text Generation
Dialogue Interaction
Q&A System
Creative Writing
Career Counseling
Health Advice
Use Cases
Dialogue Systems
Career Counseling
Provides career development advice and guidance to users
Offers personalized career suggestions based on user skills and interests
Knowledge Q&A
Health Advice
Answers questions about healthy lifestyles
Provides common-sense health improvement suggestions
Creative Generation
Game Setting Creation
Generates fantasy game settings based on user requests
Creates imaginative game worlds and characters
language:
- en license: apache-2.0 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 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: 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 model-index:
- name: Minueza-32M-Chat
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: 20.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat 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: 26.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat 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: 25.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat 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: 47.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat 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: 50.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat 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: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard
- 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:
Minueza-32M-Chat: A chat model with 32 million parameters
- Base model: Felladrin/Minueza-32M-Base
- Datasets used during SFT:
- [ChatML] databricks/databricks-dolly-15k
- [ChatML] euclaise/reddit-instruct-curated
- [ChatML] THUDM/webglm-qa
- [ChatML] starfishmedical/webGPT_x_dolly
- [ChatML] LDJnr/Capybara
- [ChatML] Open-Orca/SlimOrca-Dedup
- [ChatML] HuggingFaceH4/ultrachat_200k
- [ChatML] nvidia/HelpSteer
- [ChatML] sablo/oasst2_curated
- [ChatML] CohereForAI/aya_dataset
- Datasets used during DPO:
- License: Apache License 2.0
- Availability in other ML formats:
Recommended Prompt Format
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
Recommended Inference Parameters
do_sample: true
temperature: 0.65
top_p: 0.55
top_k: 35
repetition_penalty: 1.176
Usage Example
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 and 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 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.49 |
| AI2 Reasoning Challenge (25-Shot) | 20.39 |
| HellaSwag (10-Shot) | 26.54 |
| MMLU (5-Shot) | 25.75 |
| TruthfulQA (0-shot) | 47.27 |
| Winogrande (5-shot) | 50.99 |
| GSM8k (5-shot) | 0.00 |
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