---
language:
- en
- vi
license: mit
library_name: transformers
tags:
- ghost
pipeline_tag: text-generation
model-index:
- name: ghost-7b-v0.9.1
results:
- 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: 55.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
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: 77.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
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: 54.78
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
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: 43.96
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
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: 72.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
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: 26.91
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
---
# Model Card for Model ID
**Ghost 7B Alpha, flying, v0.9.1**
[▶️ Experience it on Colab](https://colab.research.google.com/drive/1Q0dvH79PUffRKH8VKCrqn_krKmOp1QE7?usp=sharing)
### Come on, create yourself an AI assistant, according to your wishes!
In your language, maybe Vietnamese.
Or, English.
### Let the assistant become an expert, and more.
## 📚 Model Details
### Model Description
A version to consider comprehension in generating languages other than the original language being initially trained, here is the Vietnamese language. A brief summary of the effectiveness of the **Mistral 7B** model for training with a new language is excellent and low cost.
I have started training the [Ghost 7B v0.9.0](https://huggingface.co/lamhieu/ghost-7b-v0.9.0) model again, with a smaller amount of data, it is estimated to be only about 150MB. In that data, about 70% is Vietnamese, the rest is almost English.
The approach here uses QLora for training then merges them. Also, I am very thankful to Unsloth for their features.
## Uses
To make it easier to play around with the model, I created a notebook in [Google Colab](https://drive.google.com/file/d/1jVZuQ2QbMxLMJDKjpCRDKQaIxNXNpWI-/view?usp=sharing) so people can start experimenting.
Although the amount of training data is small, it is "great". You don't need to worry too much that it won't be able to meet some of your requirements. Instead, try experimenting with the model of what you want.
One more thing, use it like you would **ChatGPT**, I've purposely tweaked it to be able to replace my app (for some tasks, and it does a good job). It's okay with both Vietnamese and English languages. It would be great to hear feedback about the experience, feel free to leave information in the discussion section.
### Summary
Setting up the system prompt will have a great impact on the performance and quality of the content generated by the model. Keep this in mind to always ensure the model is used for your intended purpose, the goal is to achieve good results but.
It's best to always set system, you can still leave it empty if you always want to set it.
## 🥇 Evaluation
### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lamhieu__ghost-7b-v0.9.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |55.10|
|AI2 Reasoning Challenge (25-Shot)|55.38|
|HellaSwag (10-Shot) |77.03|
|MMLU (5-Shot) |54.78|
|TruthfulQA (0-shot) |43.96|
|Winogrande (5-shot) |72.53|
|GSM8k (5-shot) |26.91|
### VMLU
A Vietnamese Multitask Language Understanding Benchmark Suite for Large Language Models.
Details
```json
{
"humanity": {
"administrative_law": 52.22,
"business_law": 40.22,
"civil_law": 46.11,
"criminal_law": 49.08,
"economic_law": 39.75,
"education_law": 42.17,
"elementary_history": 55.37,
"high_school_history": 36.67,
"high_school_literature": 37.78,
"history_of_world_civilization": 46.67,
"idealogical_and_moral_cultivation": 50,
"introduction_to_laws": 45.24,
"vietnamese_language_and_literature": 34.48,
"total": 43.3,
"revolutionary_policy_of_the_vietnamese_commununist_part": 51.11,
"introduction_to_vietnam_culture": 30.56,
"logic": 27.01,
"middle_school_history": 44.44,
"middle_school_literature": 50.57
},
"stem": {
"total": 34.73,
"applied_informatics": 50.56,
"computer_architecture": 33.89,
"computer_network": 43.02,
"discrete_mathematics": 31.52,
"electrical_engineering": 30.68,
"elementary_mathematics": 30,
"elementary_science": 58.89,
"high_school_biology": 38.33,
"high_school_chemistry": 28.89,
"high_school_mathematics": 26.35,
"high_school_physics": 29.44,
"introduction_to_chemistry": 27.37,
"introduction_to_physics": 31.79,
"introduction_to_programming": 36.31,
"metrology_engineer": 31.21,
"middle_school_biology": 46.47,
"middle_school_chemistry": 30.56,
"middle_school_mathematics": 30.56,
"middle_school_physics": 30,
"operating_system": 40.56,
"statistics_and_probability": 22.99
},
"total": 39.58,
"other": {
"accountant": 31.55,
"civil_servant": 42.11,
"clinical_pharmacology": 33.89,
"driving_license_certificate": 59.06,
"environmental_engineering": 28.07,
"internal_basic_medicine": 39.77,
"preschool_pedagogy": 46.08,
"tax_accountant": 22.41,
"tax_civil_servant": 47.95,
"total": 38.99
},
"social_science": {
"business_administration": 41.38,
"high_school_civil_education": 45,
"high_school_geography": 34.57,
"ho_chi_minh_ideology": 48.04,
"macroeconomics": 31.11,
"microeconomics": 37.22,
"middle_school_civil_education": 66.29,
"middle_school_geography": 48.3,
"principles_of_marxism_and_leninism": 30,
"sociology": 53.93,
"total": 43.58
}
}
```
## 📜 More Information
Model trained with **Unsloth**, many thanks.
## 📨 Model Card Contact
**Lam H** (lamhieu.vk@gmail.com)