Minueza-32M-UltraChat: A chat model with 32 million parameters
- Base model: Felladrin/Minueza-32M-Base
- Dataset: [ChatML] HuggingFaceH4/ultrachat_200k
- 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-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 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 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.97 |
| AI2 Reasoning Challenge (25-Shot) | 21.08 |
| HellaSwag (10-Shot) | 26.95 |
| MMLU (5-Shot) | 26.08 |
| TruthfulQA (0-shot) | 47.70 |
| Winogrande (5-shot) | 51.78 |
| GSM8k (5-shot) | 0.23 |