Master
Collection
Collection of LLMs trained with handcrafted dataset by @qnguyen3
•
2 items
•
Updated
Model Description
Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs.
Master-Yi-9B is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions.
Quantized Version: Here
Communitiy Quantization (Thanks to @LoneStriker)
- exl2: Master-Yi-9B-8.0bpw-h8-exl2, Master-Yi-9B-6.0bpw-h6-exl2, Master-Yi-9B-5.0bpw-h6-exl2, Master-Yi-9B-4.0bpw-h6-exl2
- GGUFs: Master-Yi-9B-GGUF
Master-Yi-9B-Vision: Coming Soon
Prompt Template
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What is the meaning of life?<|im_end|>
<|im_start|>assistant
Examples
Inference Code
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"qnguyen3/Master-Yi-9B",
torch_dtype='auto',
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("qnguyen3/Master-Yi-9B")
prompt = "What is the mearning of life?"
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024,
eos_token_id=tokenizer.eos_token_id,
temperature=0.25,
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids)[0]
print(response)
Benchmarks
Nous Benchmark:
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| Master-Yi-9B | 43.55 | 71.48 | 48.54 | 41.43 | 51.25 |
AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |35.83|± | 3.01|
| | |acc_norm|31.89|± | 2.93|
|agieval_logiqa_en | 0|acc |38.25|± | 1.91|
| | |acc_norm|37.79|± | 1.90|
|agieval_lsat_ar | 0|acc |23.04|± | 2.78|
| | |acc_norm|20.43|± | 2.66|
|agieval_lsat_lr | 0|acc |48.04|± | 2.21|
| | |acc_norm|42.75|± | 2.19|
|agieval_lsat_rc | 0|acc |61.34|± | 2.97|
| | |acc_norm|52.79|± | 3.05|
|agieval_sat_en | 0|acc |79.13|± | 2.84|
| | |acc_norm|72.33|± | 3.12|
|agieval_sat_en_without_passage| 0|acc |44.17|± | 3.47|
| | |acc_norm|42.72|± | 3.45|
|agieval_sat_math | 0|acc |52.27|± | 3.38|
| | |acc_norm|47.73|± | 3.38|
Average: 43.55%
GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |54.95|± | 1.45|
| | |acc_norm|58.70|± | 1.44|
|arc_easy | 0|acc |82.28|± | 0.78|
| | |acc_norm|81.10|± | 0.80|
|boolq | 1|acc |86.15|± | 0.60|
|hellaswag | 0|acc |59.16|± | 0.49|
| | |acc_norm|77.53|± | 0.42|
|openbookqa | 0|acc |37.40|± | 2.17|
| | |acc_norm|44.00|± | 2.22|
|piqa | 0|acc |79.00|± | 0.95|
| | |acc_norm|80.25|± | 0.93|
|winogrande | 0|acc |72.61|± | 1.25|
Average: 71.48%
TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |33.05|± | 1.65|
| | |mc2 |48.54|± | 1.54|
Average: 48.54%
Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|54.74|± | 3.62|
|bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|40.31|± | 3.06|
|bigbench_geometric_shapes | 0|multiple_choice_grade|30.36|± | 2.43|
| | |exact_str_match | 2.23|± | 0.78|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.00|± | 1.96|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.00|± | 2.87|
|bigbench_movie_recommendation | 0|multiple_choice_grade|35.00|± | 2.14|
|bigbench_navigate | 0|multiple_choice_grade|58.40|± | 1.56|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|61.80|± | 1.09|
|bigbench_ruin_names | 0|multiple_choice_grade|42.41|± | 2.34|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|31.56|± | 1.47|
|bigbench_snarks | 0|multiple_choice_grade|55.25|± | 3.71|
|bigbench_sports_understanding | 0|multiple_choice_grade|69.37|± | 1.47|
|bigbench_temporal_sequences | 0|multiple_choice_grade|27.70|± | 1.42|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|14.69|± | 0.85|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.00|± | 2.87|
Average: 41.43%
Average score: 51.25%
OpenLLM Benchmark:
| Model | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | Average |
|---|---|---|---|---|---|---|---|
| Master-Yi-9B | 61.6 | 79.89 | 69.95 | 48.59 | 77.35 | 67.48 | 67.48 |
ARC
| Task |Version| Metric | Value | |Stderr|
|-------------|------:|--------------------|-------------|---|------|
|arc_challenge| 1|acc,none | 0.59| | |
| | |acc_stderr,none | 0.01| | |
| | |acc_norm,none | 0.62| | |
| | |acc_norm_stderr,none| 0.01| | |
| | |alias |arc_challenge| | |
Average: 61.6%
HellaSwag
| Task |Version| Metric | Value | |Stderr|
|---------|------:|--------------------|---------|---|------|
|hellaswag| 1|acc,none | 0.61| | |
| | |acc_stderr,none | 0| | |
| | |acc_norm,none | 0.80| | |
| | |acc_norm_stderr,none| 0| | |
| | |alias |hellaswag| | |
Average: 79.89%
MMLU
| Task |Version| Metric | Value | |Stderr|
|----------------------------------------|-------|---------------|---------------------------------------|---|------|
|mmlu |N/A |acc,none | 0.7| | |
| | |acc_stderr,none| 0| | |
| | |alias |mmlu | | |
|mmlu_abstract_algebra | 0|alias | - abstract_algebra | | |
| | |acc,none |0.46 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_anatomy | 0|alias | - anatomy | | |
| | |acc,none |0.64 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_astronomy | 0|alias | - astronomy | | |
| | |acc,none |0.77 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_business_ethics | 0|alias | - business_ethics | | |
| | |acc,none |0.76 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_clinical_knowledge | 0|alias | - clinical_knowledge | | |
| | |acc,none |0.71 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_college_biology | 0|alias | - college_biology | | |
| | |acc,none |0.82 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_college_chemistry | 0|alias | - college_chemistry | | |
| | |acc,none |0.52 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_college_computer_science | 0|alias | - college_computer_science | | |
| | |acc,none |0.56 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_college_mathematics | 0|alias | - college_mathematics | | |
| | |acc,none |0.44 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_college_medicine | 0|alias | - college_medicine | | |
| | |acc,none |0.72 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_college_physics | 0|alias | - college_physics | | |
| | |acc,none |0.45 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_computer_security | 0|alias | - computer_security | | |
| | |acc,none |0.81 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_conceptual_physics | 0|alias | - conceptual_physics | | |
| | |acc,none |0.74 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_econometrics | 0|alias | - econometrics | | |
| | |acc,none |0.65 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_electrical_engineering | 0|alias | - electrical_engineering | | |
| | |acc,none |0.72 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_elementary_mathematics | 0|alias | - elementary_mathematics | | |
| | |acc,none |0.62 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_formal_logic | 0|alias | - formal_logic | | |
| | |acc,none |0.57 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_global_facts | 0|alias | - global_facts | | |
| | |acc,none |0.46 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_high_school_biology | 0|alias | - high_school_biology | | |
| | |acc,none |0.86 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_chemistry | 0|alias | - high_school_chemistry | | |
| | |acc,none |0.67 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_high_school_computer_science | 0|alias | - high_school_computer_science | | |
| | |acc,none |0.84 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_high_school_european_history | 0|alias | - high_school_european_history | | |
| | |acc,none |0.82 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_high_school_geography | 0|alias | - high_school_geography | | |
| | |acc,none |0.86 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_government_and_politics| 0|alias | - high_school_government_and_politics| | |
| | |acc,none |0.90 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_macroeconomics | 0|alias | - high_school_macroeconomics | | |
| | |acc,none |0.75 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_mathematics | 0|alias | - high_school_mathematics | | |
| | |acc,none |0.43 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_high_school_microeconomics | 0|alias | - high_school_microeconomics | | |
| | |acc,none |0.86 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_physics | 0|alias | - high_school_physics | | |
| | |acc,none |0.45 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_high_school_psychology | 0|alias | - high_school_psychology | | |
| | |acc,none |0.87 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_high_school_statistics | 0|alias | - high_school_statistics | | |
| | |acc,none |0.68 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_high_school_us_history | 0|alias | - high_school_us_history | | |
| | |acc,none |0.85 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_high_school_world_history | 0|alias | - high_school_world_history | | |
| | |acc,none |0.85 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_human_aging | 0|alias | - human_aging | | |
| | |acc,none |0.76 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_human_sexuality | 0|alias | - human_sexuality | | |
| | |acc,none |0.78 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_humanities |N/A |alias | - humanities | | |
| | |acc,none |0.63 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_international_law | 0|alias | - international_law | | |
| | |acc,none |0.79 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_jurisprudence | 0|alias | - jurisprudence | | |
| | |acc,none |0.79 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_logical_fallacies | 0|alias | - logical_fallacies | | |
| | |acc,none |0.80 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_machine_learning | 0|alias | - machine_learning | | |
| | |acc,none |0.52 | | |
| | |acc_stderr,none|0.05 | | |
|mmlu_management | 0|alias | - management | | |
| | |acc,none |0.83 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_marketing | 0|alias | - marketing | | |
| | |acc,none |0.89 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_medical_genetics | 0|alias | - medical_genetics | | |
| | |acc,none |0.78 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_miscellaneous | 0|alias | - miscellaneous | | |
| | |acc,none |0.85 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_moral_disputes | 0|alias | - moral_disputes | | |
| | |acc,none |0.75 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_moral_scenarios | 0|alias | - moral_scenarios | | |
| | |acc,none |0.48 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_nutrition | 0|alias | - nutrition | | |
| | |acc,none |0.77 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_other |N/A |alias | - other | | |
| | |acc,none |0.75 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_philosophy | 0|alias | - philosophy | | |
| | |acc,none |0.78 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_prehistory | 0|alias | - prehistory | | |
| | |acc,none |0.77 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_professional_accounting | 0|alias | - professional_accounting | | |
| | |acc,none |0.57 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_professional_law | 0|alias | - professional_law | | |
| | |acc,none |0.50 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_professional_medicine | 0|alias | - professional_medicine | | |
| | |acc,none |0.71 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_professional_psychology | 0|alias | - professional_psychology | | |
| | |acc,none |0.73 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_public_relations | 0|alias | - public_relations | | |
| | |acc,none |0.76 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_security_studies | 0|alias | - security_studies | | |
| | |acc,none |0.78 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_social_sciences |N/A |alias | - social_sciences | | |
| | |acc,none |0.81 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_sociology | 0|alias | - sociology | | |
| | |acc,none |0.86 | | |
| | |acc_stderr,none|0.02 | | |
|mmlu_stem |N/A |alias | - stem | | |
| | |acc,none |0.65 | | |
| | |acc_stderr,none|0.01 | | |
|mmlu_us_foreign_policy | 0|alias | - us_foreign_policy | | |
| | |acc,none |0.92 | | |
| | |acc_stderr,none|0.03 | | |
|mmlu_virology | 0|alias | - virology | | |
| | |acc,none |0.58 | | |
| | |acc_stderr,none|0.04 | | |
|mmlu_world_religions | 0|alias | - world_religions | | |
| | |acc,none |0.82 | | |
| | |acc_stderr,none|0.03 | | |
Average: 69.95%
TruthfulQA
| Task |Version| Metric | Value | |Stderr|
|--------------|-------|-----------------------|-----------------|---|------|
|truthfulqa |N/A |bleu_acc,none | 0.45| | |
| | |bleu_acc_stderr,none | 0.02| | |
| | |rouge1_acc,none | 0.45| | |
| | |rouge1_acc_stderr,none | 0.02| | |
| | |rouge2_diff,none | 0.92| | |
| | |rouge2_diff_stderr,none| 1.07| | |
| | |bleu_max,none | 23.77| | |
| | |bleu_max_stderr,none | 0.81| | |
| | |rouge2_acc,none | 0.38| | |
| | |rouge2_acc_stderr,none | 0.02| | |
| | |acc,none | 0.41| | |
| | |acc_stderr,none | 0.01| | |
| | |rougeL_diff,none | 1.57| | |
| | |rougeL_diff_stderr,none| 0.93| | |
| | |rougeL_acc,none | 0.46| | |
| | |rougeL_acc_stderr,none | 0.02| | |
| | |bleu_diff,none | 1.38| | |
| | |bleu_diff_stderr,none | 0.75| | |
| | |rouge2_max,none | 33.01| | |
| | |rouge2_max_stderr,none | 1.05| | |
| | |rouge1_diff,none | 1.72| | |
| | |rouge1_diff_stderr,none| 0.92| | |
| | |rougeL_max,none | 45.25| | |
| | |rougeL_max_stderr,none | 0.92| | |
| | |rouge1_max,none | 48.29| | |
| | |rouge1_max_stderr,none | 0.90| | |
| | |alias |truthfulqa | | |
|truthfulqa_gen| 3|bleu_max,none | 23.77| | |
| | |bleu_max_stderr,none | 0.81| | |
| | |bleu_acc,none | 0.45| | |
| | |bleu_acc_stderr,none | 0.02| | |
| | |bleu_diff,none | 1.38| | |
| | |bleu_diff_stderr,none | 0.75| | |
| | |rouge1_max,none | 48.29| | |
| | |rouge1_max_stderr,none | 0.90| | |
| | |rouge1_acc,none | 0.45| | |
| | |rouge1_acc_stderr,none | 0.02| | |
| | |rouge1_diff,none | 1.72| | |
| | |rouge1_diff_stderr,none| 0.92| | |
| | |rouge2_max,none | 33.01| | |
| | |rouge2_max_stderr,none | 1.05| | |
| | |rouge2_acc,none | 0.38| | |
| | |rouge2_acc_stderr,none | 0.02| | |
| | |rouge2_diff,none | 0.92| | |
| | |rouge2_diff_stderr,none| 1.07| | |
| | |rougeL_max,none | 45.25| | |
| | |rougeL_max_stderr,none | 0.92| | |
| | |rougeL_acc,none | 0.46| | |
| | |rougeL_acc_stderr,none | 0.02| | |
| | |rougeL_diff,none | 1.57| | |
| | |rougeL_diff_stderr,none| 0.93| | |
| | |alias | - truthfulqa_gen| | |
|truthfulqa_mc1| 2|acc,none | 0.33| | |
| | |acc_stderr,none | 0.02| | |
| | |alias | - truthfulqa_mc1| | |
|truthfulqa_mc2| 2|acc,none | 0.49| | |
| | |acc_stderr,none | 0.02| | |
| | |alias | - truthfulqa_mc2| | |
Average: 48.59%
Winogrande
| Task |Version| Metric | Value | |Stderr|
|----------|------:|---------------|----------|---|------|
|winogrande| 1|acc,none | 0.77| | |
| | |acc_stderr,none| 0.01| | |
| | |alias |winogrande| | |
Average: 77.35%
GSM8K
|Task |Version| Metric |Value| |Stderr|
|-----|------:|-----------------------------------|-----|---|------|
|gsm8k| 3|exact_match,strict-match | 0.67| | |
| | |exact_match_stderr,strict-match | 0.01| | |
| | |exact_match,flexible-extract | 0.68| | |
| | |exact_match_stderr,flexible-extract| 0.01| | |
| | |alias |gsm8k| | |
Average: 67.48%
Average score: 67.48%
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