Pip Code Bandit by PipableAI

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Pip Code Bandit Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Pip Code Bandit (PipableAI/pip-code-bandit)

Pip Code Bandit Parameters and Internals

Model Type 
text generation, function calling, instruction tuning
Use Cases 
Areas:
research, commercial applications
Primary Use Cases:
complex planning of sequential function calls, function calling, code generation, doc generation, sql generation
Supported Languages 
python (full proficiency), java (full proficiency), cpp (full proficiency), sql (full proficiency)
Training Details 
Data Sources:
simulated environments, code datasets, function documentation, open source datasets
Methodology:
RL tuned
Context Length:
16384
LLM NamePip Code Bandit
Repository 🤗https://huggingface.co/PipableAI/pip-code-bandit 
Model Size1.3b
Required VRAM2.7 GB
Updated2025-06-01
MaintainerPipableAI
Model Typellama
Instruction-BasedYes
Model Files  2.7 GB
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length16384
Model Max Length16384
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<|end▁of▁sentence|>
Vocabulary Size32256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than PipableAI/pip-code-bandit.

Rank the Pip Code Bandit Capabilities

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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 v20241227