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Dictalm2.0 AWQ by dicta-il

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Dictalm2.0 AWQ is an open-source language model by dicta-il. Features: 1.2b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.14.

  Arxiv:2407.07080   4-bit   Autotrain compatible   Awq   En   He   Mistral   Pretrained   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/dicta-il/dictalm2.0-AWQ 

Dictalm2.0 AWQ Benchmarks

LLME Score: 0.13776
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Dictalm2.0 AWQ (dicta-il/dictalm2.0-AWQ)
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Dictalm2.0 AWQ Parameters and Internals

Model Type 
text-generation
Use Cases 
Limitations:
Does not have any moderation mechanisms.
Additional Notes 
DictaLM 2.0 is specialized in Hebrew text processing.
Supported Languages 
en (Yes), he (Yes)
Training Details 
Data Sources:
naturally occurring text
Data Volume:
over 190B tokens
LLM NameDictalm2.0 AWQ
Repository 🤗https://huggingface.co/dicta-il/dictalm2.0-AWQ 
Model Size1.2b
Required VRAM4.2 GB
Updated2025-11-13
Maintainerdicta-il
Model Typemistral
Model Files  4.2 GB
Supported Languagesen he
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size33152
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than dicta-il/dictalm2.0-AWQ.

Rank the Dictalm2.0 AWQ 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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