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MelloGPT AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:quantized:steve-cse...   Base model:steve-cse/mellogpt   Conversational Dataset:nbertagnolli/counsel-c...   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/MelloGPT-AWQ 

MelloGPT AWQ Benchmarks

LLME Score: 0.16049
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MelloGPT AWQ (TheBloke/MelloGPT-AWQ)

MelloGPT AWQ Parameters and Internals

Model Type 
mistral
Use Cases 
Areas:
mental health support
Primary Use Cases:
mental health counseling
Considerations:
Ethical considerations, privacy concerns, and sensitivity to the nuances of mental health conversations
Additional Notes 
AWQ (4-bit quantization) for efficient inference.
Training Details 
Data Sources:
counsel-chat
Methodology:
fine-tuned on mental health counseling conversations
Input Output 
Input Format:
{prompt}
LLM NameMelloGPT AWQ
Repository 🤗https://huggingface.co/TheBloke/MelloGPT-AWQ 
Model NameMelloGPT
Model CreatorSteve Boby George
Base Model(s)  MelloGPT   steve-cse/MelloGPT
Model Size1.2b
Required VRAM4.2 GB
Updated2024-12-26
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/MelloGPT-AWQ.

Rank the MelloGPT AWQ Capabilities

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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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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