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 Evaluation Results

Detailed results can be found 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.

{
  "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

• Datasets: 5CD-AI, vilm.
• Library: unsloth

Model Card Contact

Lam H (lamhieu.vk@gmail.com)