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Gemma 7B AWQ by TechxGenus

By TechxGenus · 6 downloads
HF Repo ↗ Copy URL

Gemma 7B AWQ is an open-source language model by TechxGenus. Features: 7b LLM, VRAM: 7.2GB, Context: 8K, License: other, Quantized, LLM Explorer Score: 0.11.

  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2305.14314   Arxiv:2312.11805   4-bit   Awq   Endpoints compatible   Gemma   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-7b-AWQ 

Gemma 7B AWQ Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Content Creation, Research and Education
Applications:
Text Generation, Chatbots and Conversational AI, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Generate creative text formats, Power conversational interfaces, Generate summaries of text
Limitations:
Bias or gaps in training data, Limited factual accuracy, Lack of common sense reasoning
Considerations:
Responsible use guidelines are outlined.
Additional Notes 
Gemma models democratize access to state-of-the-art AI models enabling innovation for everyone.
Supported Languages 
English (high)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Safety Evaluation 
Methodologies:
Red-teaming, Structured evaluations
Findings:
Within acceptable thresholds for meeting internal policies
Risk Categories:
Child safety, Content safety, Representational harms, Memorization, Large-scale harms
Ethical Considerations:
These models underwent robust internal evaluations and mitigation strategies were proposed.
Responsible Ai Considerations 
Fairness:
Input data pre-processing and posterior evaluations were conducted to scrutinize bias.
Transparency:
Details on the model's architecture, capabilities, limitations, and evaluation processes are provided.
Accountability:
Developers and users are encouraged to exercise caution and implement appropriate safeguards as per guidelines.
Mitigation Strategies:
Continuous monitoring, human review, and exploration of de-biasing techniques during training.
Input Output 
Input Format:
Text string, such as a question, a prompt, or a document.
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 7B AWQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-7b-AWQ 
Model Size7b
Required VRAM7.2 GB
Updated2026-07-30
MaintainerTechxGenus
Model Typegemma
Model Files  6.6 GB: 1-of-2   0.6 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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