Quantization made by Richard Erkhov.

ghost-7b-v0.9.0 - GGUF

Model creator: https://huggingface.co/ghost-x/
Original model: https://huggingface.co/ghost-x/ghost-7b-v0.9.0/

Name	Quant method	Size
ghost-7b-v0.9.0.Q2_K.gguf	Q2_K	2.53GB
ghost-7b-v0.9.0.IQ3_XS.gguf	IQ3_XS	2.81GB
ghost-7b-v0.9.0.IQ3_S.gguf	IQ3_S	2.96GB
ghost-7b-v0.9.0.Q3_K_S.gguf	Q3_K_S	2.95GB
ghost-7b-v0.9.0.IQ3_M.gguf	IQ3_M	3.06GB
ghost-7b-v0.9.0.Q3_K.gguf	Q3_K	3.28GB
ghost-7b-v0.9.0.Q3_K_M.gguf	Q3_K_M	3.28GB
ghost-7b-v0.9.0.Q3_K_L.gguf	Q3_K_L	3.56GB
ghost-7b-v0.9.0.IQ4_XS.gguf	IQ4_XS	3.67GB
ghost-7b-v0.9.0.Q4_0.gguf	Q4_0	3.83GB
ghost-7b-v0.9.0.IQ4_NL.gguf	IQ4_NL	3.87GB
ghost-7b-v0.9.0.Q4_K_S.gguf	Q4_K_S	3.86GB
ghost-7b-v0.9.0.Q4_K.gguf	Q4_K	4.07GB
ghost-7b-v0.9.0.Q4_K_M.gguf	Q4_K_M	4.07GB
ghost-7b-v0.9.0.Q4_1.gguf	Q4_1	4.24GB
ghost-7b-v0.9.0.Q5_0.gguf	Q5_0	4.65GB
ghost-7b-v0.9.0.Q5_K_S.gguf	Q5_K_S	4.65GB
ghost-7b-v0.9.0.Q5_K.gguf	Q5_K	4.78GB
ghost-7b-v0.9.0.Q5_K_M.gguf	Q5_K_M	4.78GB
ghost-7b-v0.9.0.Q5_1.gguf	Q5_1	5.07GB
ghost-7b-v0.9.0.Q6_K.gguf	Q6_K	5.53GB
ghost-7b-v0.9.0.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language:

en
vi license: mit library_name: transformers tags:
ghost pipeline_tag: text-generation base_model: HuggingFaceH4/zephyr-7b-beta widget:
text: '<|system|>

You are a helpful assistant.

<|user|>

Thông tin về Peristernia despecta

<|assistant|>

' output: text: Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng sống ở biển trong họ Fasciolariidae.

model-index: - name: lamhieu/ghost-7b-v0.9.0 results: - task: type: text-generation dataset: name: VMLU type: vmlu_v1.5 metrics: - type: avg value: 36.06 name: Average verified: true - type: stem value: 33.54 name: STEM verified: true - type: ss value: 38.74 name: Social science verified: true - type: hm value: 37.15 name: Humanities verified: true - type: ot value: 36.78 name: Other verified: true - task: type: text-generation dataset: name: Open LLM Leaderboard type: open_llm_leaderboard metrics: - type: avg value: 56.89 name: Average verified: true - type: arc value: 53.07 name: ARC verified: true - type: hs value: 77.93 name: HellaSwag verified: true - type: hs value: 77.93 name: HellaSwag verified: true - type: mmlu value: 55.09 name: MMLU verified: true - type: wg value: 73.72 name: Winogrande verified: true - type: gsm8k value: 33.74 name: GSM8K verified: true source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - 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: 53.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 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: 77.93 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 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: 55.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 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: 47.79 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 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: 73.72 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 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: 33.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: 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)