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Merak 7B V4 by Ichsan2895

 »  All LLMs  »  Ichsan2895  »  Merak 7B V4   URL Share it on

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  Arxiv:2305.14314   Arxiv:2306.02707   Autotrain compatible   Conversational Dataset:ichsan2895/alpaca-gpt4... Dataset:ichsan2895/oasst top1 ...   Dataset:wikipedia   En   Endpoints compatible   Id   Mistral   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/Ichsan2895/Merak-7B-v4 

Merak 7B V4 Benchmarks

LLME Score: 0.11893
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Merak 7B V4 (Ichsan2895/Merak-7B-v4)
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Merak 7B V4 Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, AI development
Applications:
text generation, chatbots for Indonesian language
Primary Use Cases:
Text-based AI Applications
Limitations:
does not work optimally with BitsandBytes 4-bit Quantization
Additional Notes 
Created with help from Axolotl fine-tuning tool.
Supported Languages 
id (high proficiency), en (medium proficiency)
Training Details 
Data Sources:
wikipedia, Ichsan2895/OASST_Top1_Indonesian, Ichsan2895/alpaca-gpt4-indonesian
Data Volume:
200k to 600k ID Wikipedia articles
Methodology:
Fine-tuning with QLoRA and Full Parameter Finetuning (FFT)
Training Time:
1 epoch with QLoRA, 4 epochs with FFT
Hardware Used:
utilizes BitsAndBytes and can run with 16 GB VRAM
Model Architecture:
Fine-tuned on top of Mistral-7B-OpenOrca using QLoRA
Input Output 
Input Format:
Chat format using roles (system, user)
Accepted Modalities:
text
Output Format:
Text response
Performance Tips:
For better performance, avoid using BitsandBytes 4-bit Quantization
Release Notes 
Version:
v4
Notes:
Used Mistral-7B-OpenOrca instead of Llama-2-Chat-HF; FFT applied with extra resources.
Version:
v3
Notes:
Fine-tuning with additional datasets.
Version:
v2
Notes:
Fine-tuning with 600k ID Wikipedia articles.
Version:
v1
Notes:
Initial model with 200k selected and cleaned ID Wikipedia articles.
LLM NameMerak 7B V4
Repository 🤗https://huggingface.co/Ichsan2895/Merak-7B-v4 
Model Size7b
Required VRAM28.9 GB
Updated2025-10-02
MaintainerIchsan2895
Model Typemistral
Model Files  5.0 GB: 1-of-6   4.9 GB: 2-of-6   5.0 GB: 3-of-6   5.0 GB: 4-of-6   4.8 GB: 5-of-6   4.2 GB: 6-of-6
Supported Languagesid en
Model ArchitectureMistralForCausalLM
Licensecc-by-nc-sa-4.0
Context Length32768
Model Max Length32768
Transformers Version4.34.1
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32002
Torch Data Typefloat32

Quantized Models of the Merak 7B V4

Model
Likes
Downloads
VRAM
USK Mistral 7B Unsloth GGUF0184 GB
Merak 7B V4 GPTQ234 GB

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Note: green Score (e.g. "73.2") means that the model is better than Ichsan2895/Merak-7B-v4.

Rank the Merak 7B V4 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241124
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