language, license, library_name, tags, pipeline_tag, base_model, widget, model-index

language	license	library_name	tags	pipeline_tag	base_model	widget	model-index
en vi	mit	transformers	ghost	text-generation	HuggingFaceH4/zephyr-7b-beta	text output <|system|> You are a helpful assistant.</s> <|user|> Thông tin về Peristernia despecta</s> <|assistant|> text Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng sống ở biển trong họ Fasciolariidae.	name results lamhieu/ghost-7b-v0.9.0 task dataset metrics type text-generation name type VMLU vmlu_v1.5 type value name verified avg 36.06 Average true type value name verified stem 33.54 STEM true type value name verified ss 38.74 Social science true type value name verified hm 37.15 Humanities true type value name verified ot 36.78 Other true task dataset metrics source type text-generation name type Open LLM Leaderboard open_llm_leaderboard type value name verified avg 56.89 Average true type value name verified arc 53.07 ARC true type value name verified hs 77.93 HellaSwag true type value name verified hs 77.93 HellaSwag true type value name verified mmlu 55.09 MMLU true type value name verified wg 73.72 Winogrande true type value name verified gsm8k 33.74 GSM8K true url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 53.07 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 77.93 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 55.09 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 47.79 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 73.72 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 33.74 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 Open LLM Leaderboard

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

Metric	Value
Avg.	56.89
AI2 Reasoning Challenge (25-Shot)	53.07
HellaSwag (10-Shot)	77.93
MMLU (5-Shot)	55.09
TruthfulQA (0-shot)	47.79
Winogrande (5-shot)	73.72
GSM8k (5-shot)	33.74

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

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

Model Card Contact

Lam H (lamhieu.vk@gmail.com)