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TableLLM 7B by RUCKBReasoning

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TableLLM 7B is an open-source language model by RUCKBReasoning. Features: 7b LLM, VRAM: 13.5GB, Context: 16K, License: llama2, Instruction-Based, Code Generating, LLM Explorer Score: 0.12.

  Arxiv:2403.19318   Code   Codegen Dataset:ruckbreasoning/tablell...   Deploy:azure   En   Endpoints compatible   Instruct   Llama   Qa   Region:us   Safetensors   Sharded   Table   Tensorflow
Model Card on HF 🤗: https://huggingface.co/RUCKBReasoning/TableLLM-7b 

TableLLM 7B Benchmarks

LLME Score: 0.11934
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
TableLLM 7B (RUCKBReasoning/TableLLM-7b)
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TableLLM 7B Parameters and Internals

Model Type 
tabular data manipulation, text generation, code generation
Additional Notes 
TableLLM is designed to handle tabular data manipulation tasks efficiently, providing either a code solution or a direct text answer depending on the scenario.
Supported Languages 
en (high proficiency)
Training Details 
Data Sources:
RUCKBReasoning/TableLLM-SFT
Methodology:
The models are fine-tuned based on CodeLlama-7b-Instruct-hf and CodeLlama-13b-Instruct-hf.
LLM NameTableLLM 7B
Repository 🤗https://huggingface.co/RUCKBReasoning/TableLLM-7b 
Model Size7b
Required VRAM13.5 GB
Updated2026-04-10
MaintainerRUCKBReasoning
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   3.6 GB: 3-of-3
Supported Languagesen
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token[PAD]
Vocabulary Size32016
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than RUCKBReasoning/TableLLM-7b.

Rank the TableLLM 7B 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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