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CausalLM 7B GPTQ by TheBloke

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  4-bit   Autotrain compatible   Base model:causallm/7b Base model:quantized:causallm/...   Dataset:baai/coig   Dataset:fnlp/moss-003-sft-data Dataset:garage-baind/open-plat... Dataset:jondurbin/airoboros-3.... Dataset:josephuscheung/guanaco...   Dataset:ldjnr/puffin Dataset:liuhaotian/llava-instr...   Dataset:liwu/mnbvc   Dataset:meta-math/metamathqa Dataset:milashkaarshif/moegirl...   Dataset:open-orca/openorca   Dataset:openbmb/llava zh   Dataset:ryokoai/fandom23k   Dataset:ryokoai/sharegpt52k   Dataset:stingning/ultrachat   Dataset:teknium/openhermes Dataset:tigerresearch/tigerbot...   Dataset:wiki lingua   Dataset:wikipedia Dataset:wizardlm/wizardlm evol...   En   Gptq   Instruct   Llama   Llama2   Quantized   Qwen   Region:us   Safetensors   Zh
Model Card on HF 🤗: https://huggingface.co/TheBloke/CausalLM-7B-GPTQ 

CausalLM 7B GPTQ Benchmarks

LLME Score: 0.11338
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CausalLM 7B GPTQ (TheBloke/CausalLM-7B-GPTQ)
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CausalLM 7B GPTQ Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Speculative Sampling
Applications:
Research, Text Generation
Primary Use Cases:
Multimodal Capabilities
Limitations:
May produce hallucinations or unreliable outputs
Considerations:
Manual checks required for safety
Additional Notes 
Developed with grants from Andreessen Horowitz (a16z)
Supported Languages 
en (general), zh (general)
Training Details 
Data Sources:
JosephusCheung/GuanacoDataset, Open-Orca/OpenOrca, stingning/ultrachat, meta-math/MetaMathQA, liuhaotian/LLaVA-Instruct-150K, jondurbin/airoboros-3.1, WizardLM/WizardLM_evol_instruct_V2_196k, RyokoAI/ShareGPT52K, RyokoAI/Fandom23K, milashkaarshif/MoeGirlPedia_wikitext_raw_archive, wikipedia, wiki_lingua, fnlp/moss-003-sft-data, garage-bAInd/Open-Platypus, LDJnr/Puffin, openbmb/llava_zh, BAAI/COIG, TigerResearch/tigerbot-zhihu-zh-10k, liwu/MNBVC, teknium/openhermes
Data Volume:
1.3B tokens
Methodology:
Identical structure to LLaMA2, using synthetic data
Model Architecture:
LLaMA2 architecture without scaling of RoPE
Safety Evaluation 
Risk Categories:
misinformation, bias, objectionable content, pornography, violence, offensive language
Ethical Considerations:
Model trained on unfiltered internet data
Responsible Ai Considerations 
Fairness:
Synthetic data utilized for some language variants
Accountability:
Developers have not vetted all content
Mitigation Strategies:
Users advised to filter certain keywords
Input Output 
Input Format:
ChatML prompt format
Accepted Modalities:
text
Output Format:
text
LLM NameCausalLM 7B GPTQ
Repository 🤗https://huggingface.co/TheBloke/CausalLM-7B-GPTQ 
Model NameCausalLM 7B
Model CreatorCausalLM
Base Model(s)  7B   CausalLM/7B
Model Size7b
Required VRAM5.9 GB
Updated2025-09-23
MaintainerTheBloke
Model Typellama
Instruction-BasedYes
Model Files  5.9 GB
Supported Languagesen zh
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureLlamaForCausalLM
Licensewtfpl
Context Length8192
Model Max Length8192
Transformers Version4.34.0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size151936
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/CausalLM-7B-GPTQ.

Rank the CausalLM 7B GPTQ 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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