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QCammel 70 X GPTQ by TheBloke

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QCammel 70 X GPTQ is an open-source language model by TheBloke. Features: 69b LLM, VRAM: 35.3GB, Context: 4K, License: other, Quantized, LLM Explorer Score: 0.08.

  Arxiv:2302.70971   Arxiv:2305.12031   Arxiv:2305.14314   4-bit Base model:augtoma/qcammel-70-... Base model:quantized:augtoma/q...   En   Gptq   Llama   Llama2   Pytorch   Qcammel-70   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/qCammel-70-x-GPTQ 

QCammel 70 X GPTQ Benchmarks

LLME Score: 0.08452
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
QCammel 70 X GPTQ (TheBloke/qCammel-70-x-GPTQ)
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QCammel 70 X GPTQ Parameters and Internals

Model Type 
llama
Additional Notes 
Optimized for academic medical knowledge and instruction-following capabilities.
Training Details 
Methodology:
The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA.
Model Architecture:
qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture.
Input Output 
Input Format:
Text input.
Output Format:
Text output.
LLM NameQCammel 70 X GPTQ
Repository 🤗https://huggingface.co/TheBloke/qCammel-70-x-GPTQ 
Model NameqCammel 70
Model Creatoraugtoma
Base Model(s)  QCammel 70 X   augtoma/qCammel-70-x
Model Size69b
Required VRAM35.3 GB
Updated2026-04-20
MaintainerTheBloke
Model Typellama
Model Files  35.3 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length4096
Model Max Length4096
Transformers Version4.32.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/qCammel-70-x-GPTQ.

Rank the QCammel 70 X GPTQ 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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