metadata
library_name: transformers
license: mit
language:
- en
- vi
pipeline_tag: text-generation
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- ghost
model-index:
- name: lamhieu/ghost-7b-v0.9.0
results:
- task:
type: text-generation
dataset:
type: vmlu_v1.5
name: VMLU
metrics:
- name: Average
type: avg
value: 36.06
verified: true
- name: STEM
type: stem
value: 33.54
verified: true
- name: Social science
type: ss
value: 38.74
verified: true
- name: Humanities
type: hm
value: 37.15
verified: true
- name: Other
type: ot
value: 36.78
verified: true
- task:
type: text-generation
dataset:
type: open_llm_leaderboard
name: Open LLM Leaderboard
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
metrics:
- name: Average
type: avg
value: 56.89
verified: true
- name: ARC
type: arc
value: 53.07
verified: true
- name: HellaSwag
type: hs
value: 77.93
verified: true
- name: HellaSwag
type: hs
value: 77.93
verified: true
- name: MMLU
type: mmlu
value: 55.09
verified: true
- name: Winogrande
type: wg
value: 73.72
verified: true
- name: GSM8K
type: gsm8k
value: 33.74
verified: true
widget:
- text: |
<|system|>
You are a pirate chatbot who always responds with Arr!</s>
<|user|>
There's a llama on my lawn, how can I get rid of him?</s>
<|assistant|>
output:
text: >-
Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
to gather some carrots and hay, and then lure the llama away with the
promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
enjoy the peace and quiet once again. But beware, me hearty, for there
may be more llamas where that one came from! Arr!
Model Card for Model ID
Ghost 7B Alpha, flying, v0.9.0
Model Details
Model Description
This model is fine tuned from HuggingFaceH4/zephyr-7b-beta on a small synthetic datasets (about 200MB) for 50% English and 50% Vietnamese.
- Developed by: Lam H
- Language(s) (NLP): English, Vietnamese
- License: MIT
- Finetuned from model: HuggingFaceH4/zephyr-7b-beta
Uses
This model supports both conversation chat and tasks. Feel free to experiment and don't limit your creativity.
The simplest way to try it is to use the pipeline from transformers.
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="lamhieu/ghost-7b-v0.9.0",
torch_dtype=torch.bfloat16,
)
You can then try any of the sample codes below, formatted using the chat template.
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "nói tôi biết bệnh dịch hạch ở châu Âu do khuẩn nào gây ra"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = pipe.model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
# Bệnh dịch hạch ở châu Âu do khuẩn gây ra là do khuẩn Yersinia pestis.
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Thông tin về Peristernia despecta"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = pipe.model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
# Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng sống ở biển trong họ Fasciolariidae.
# ...
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "do u know vietnam ?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = pipe.model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
# Yes, I have knowledge about Vietnam. Vietnam is a country in Southeast Asia, bordered by China to the north, Laos and Cambodia to the west, and the South China Sea to the east and south. Its capital city is Hanoi, and its largest city is Ho Chi Minh City (formerly known as Saigon). Vietnam has a population of approximately 100 million people and a diverse cultural heritage influenced by both Chinese and French colonialism. The country has a rich history, including periods of independence, colonization, and resistance, and has experienced significant economic growth in recent years.
messages = [
{"role": "system", "content": "You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old."},
{"role": "user", "content": "Tôi yêu em nhiều hơn em nghĩ.\n\nWhich language is this?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = pipe.model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
# This is Vietnamese language. Vietnamese is a language spoken mainly in Vietnam and by the Vietnamese diaspora in many other countries. The sentence you provided means "I love you more than you think." It's like you have more love for someone than they realize.
Another example of what you can use to chat multiple turns.
messages = [
# {"role": "system", "content": "You are a helpful and knowledgeable assistant. You like to help and always give honest information, in its original language. In communication, you are always respectful, equal and promote positive behavior."},
{"role": "system", "content": "You are a helpful assistant."}, # Describe to your assistant, anything.
{"role": "user", "content": "Bla bla bla"},
{"role": "assistant", "content": "Bla bla bla"},
{"role": "user", "content": "Bla bla bla"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = pipe.model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
Evaluation
Results
Open LLM Leaderboard
This is the evaluation result of the OpenLLM Leaderboard evaluation suite, you can find it here.
VMLU
Below are the results evaluated with the VMLU evaluation suite, which is often used to evaluate models that work with Vietnamese.
Note: the results are run with the model in 4bit quantization, I'm not sure if it has any loss in results or not, if someone can help me run it with full it would be great.
Details
{
"stem": {
"elementary_mathematics": 32.22,
"elementary_science": 56.11,
"high_school_biology": 32.78,
"high_school_chemistry": 27.78,
"high_school_mathematics": 33.78,
"high_school_physics": 26.11,
"introduction_to_chemistry": 26.82,
"introduction_to_physics": 33.53,
"introduction_to_programming": 39.66,
"metrology_engineer": 36.17,
"middle_school_biology": 40,
"middle_school_chemistry": 26.67,
"middle_school_mathematics": 27.78,
"middle_school_physics": 27.22,
"operating_system": 38.33,
"statistics_and_probability": 18.39,
"total": 33.54,
"applied_informatics": 47.78,
"computer_architecture": 36.11,
"computer_network": 41.34,
"discrete_mathematics": 29.7,
"electrical_engineering": 26.14
},
"other": {
"total": 36.78,
"accountant": 29.17,
"civil_servant": 29.82,
"clinical_pharmacology": 35.56,
"driving_license_certificate": 56.73,
"environmental_engineering": 32.16,
"internal_basic_medicine": 36.84,
"preschool_pedagogy": 45.1,
"tax_accountant": 24.71,
"tax_civil_servant": 40.94
},
"total": 36.06,
"humanity": {
"introduction_to_vietnam_culture": 31.11,
"logic": 28.16,
"middle_school_history": 38.33,
"administrative_law": 32.22,
"revolutionary_policy_of_the_vietnamese_commununist_part": 40.56,
"vietnamese_language_and_literature": 35.06,
"total": 37.15,
"middle_school_literature": 36.21,
"business_law": 38.55,
"civil_law": 48.33,
"criminal_law": 37.42,
"economic_law": 38.51,
"education_law": 36.75,
"elementary_history": 35.03,
"high_school_history": 27.78,
"high_school_literature": 32.78,
"history_of_world_civilization": 43.33,
"idealogical_and_moral_cultivation": 39.44,
"introduction_to_laws": 49.21
},
"social_science": {
"business_administration": 37.36,
"high_school_civil_education": 42.78,
"high_school_geography": 38.27,
"ho_chi_minh_ideology": 40.22,
"macroeconomics": 27.78,
"microeconomics": 36.67,
"middle_school_civil_education": 51.69,
"middle_school_geography": 32.65,
"principles_of_marxism_and_leninism": 35.56,
"sociology": 44.38,
"total": 38.74
}
}
More Information
Many thanks for
Model Card Contact
Lam H ([email protected])