metadata
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
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
name: Open LLM Leaderboard
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 |