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Merged 4bit Tiny Llama Gc2 by oasic

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Merged 4bit Tiny Llama Gc2 is an open-source language model by oasic. Features: 1.1b LLM, VRAM: 0.8GB, Context: 4K, License: apache-2.0, Quantized, Merged, LLM Explorer Score: 0.13.

  Merged Model   4-bit   4bit Base model:quantized:unsloth/t... Base model:unsloth/tinyllama-b...   Bitsandbytes   En   Endpoints compatible   Llama   Quantized   Region:us   Safetensors   Sft   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/oasic/merged-4bit-tiny-llama-gc2 

Merged 4bit Tiny Llama Gc2 Benchmarks

LLME Score: 0.13026
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Merged 4bit Tiny Llama Gc2 (oasic/merged-4bit-tiny-llama-gc2)
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Merged 4bit Tiny Llama Gc2 Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, llama, trl, sft
Additional Notes 
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
LLM NameMerged 4bit Tiny Llama Gc2
Repository 🤗https://huggingface.co/oasic/merged-4bit-tiny-llama-gc2 
Base Model(s)  Tinyllama Bnb 4bit   unsloth/tinyllama-bnb-4bit
Merged ModelYes
Model Size1.1b
Required VRAM0.8 GB
Updated2026-05-23
Maintaineroasic
Model Typellama
Model Files  0.8 GB
Supported Languagesen
Quantization Type4bit
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

Best Alternatives to Merged 4bit Tiny Llama Gc2

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...E TinyLlama 1.1B 32K Base 4bit32K / 0.6 GB401
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...1B 32K Instruct 3.0bpw H6 EXL232K / 0.5 GB50
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...lama PY CODER 4bit Lora 4k V124K / 2.2 GB80
Tinyllama Coder Py V144K / 2.2 GB90
...icipal Prediction Merged Model4K / 2.2 GB60
TiamaPY V274K / 2.2 GB50
TiamaPY 1.1B V244K / 2.2 GB80
Note: green Score (e.g. "73.2") means that the model is better than oasic/merged-4bit-tiny-llama-gc2.

Rank the Merged 4bit Tiny Llama Gc2 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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