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Tinyllama PY CODER 4bit Lora 4K V5 by Ramikan-BR

 »  All LLMs  »  Ramikan-BR  »  Tinyllama PY CODER 4bit Lora 4K V5   URL Share it on

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  4bit   Autotrain compatible Base model:quantized:ramikan-b... Base model:ramikan-br/tinyllam...   Codegen   Conversational   En   Endpoints compatible   Gguf   Llama   Lora   Pytorch   Quantized   Region:us   Safetensors   Sft   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v5 

Tinyllama PY CODER 4bit Lora 4k V5 Benchmarks

ARC: 28.24 vs 96.7 (so35)-70.8%
HellaSwag: 43.54 vs 95.3 (gpt4)-54.3%
MMLU: 24.13 vs 88.3 (so35)-72.7%
TruthfulQA: 43.69 vs 59 (gpt4)-25.9%
WinoGrande: 54.3 vs 87.5 (gpt4)-37.9%
GSM8K: 0.23 vs 96.4 (so35)-99.8%
LLME Score: 0.24294
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Tinyllama PY CODER 4bit Lora 4K V5 (Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v5)

Tinyllama PY CODER 4bit Lora 4K V5 Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, llama, trl, sft
Training Details 
Methodology:
Trained 2x faster with Unsloth and Huggingface's TRL library
LLM NameTinyllama PY CODER 4bit Lora 4k V5
Repository 🤗https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v5 
Base Model(s)  ...llama PY CODER 4bit Lora 4k V4   Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v4
Model Size1.1b
Required VRAM0.7 GB
Updated2024-12-14
MaintainerRamikan-BR
Model Typellama
Model Files  2.2 GB   2.2 GB   0.7 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf|4bit
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
LoRA ModelYes
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v5.

Rank the Tinyllama PY CODER 4bit Lora 4K V5 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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