What are the hardware requirements for Pip Code Bandit?

Pip Code Bandit requires approximately 2.7 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Pip Code Bandit and how large is it?

Pip Code Bandit is developed by PipableAI, a model with 1.3b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Pip Code Bandit?

Pip Code Bandit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pip Code Bandit by PipableAI — VRAM 2.7GB, 16K context

Name: Pip Code Bandit
Author: PipableAI

Pip Code Bandit is an open-source language model by PipableAI. Features: 1.3b LLM, VRAM: 2.7GB, Context: 16K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.12.

Model Card on HF 🤗: https://huggingface.co/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 Name	Pip Code Bandit
Repository 🤗	https://huggingface.co/PipableAI/pip-code-bandit
Model Size	1.3b
Required VRAM	2.7 GB
Updated	2026-07-18
Maintainer	PipableAI
Model Type	llama
Instruction-Based	Yes
Model Files	2.7 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	16384
Model Max Length	16384
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<｜end▁of▁sentence｜>
Vocabulary Size	32256
Torch Data Type	bfloat16

Rank the Pip Code Bandit capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Pip Code Bandit

Best Alternatives	Context / RAM	Downloads	Likes
Deepseek Coder 1.3B Instruct	16K / 2.7 GB	92255	176
Speechless Coder Ds 1.3B	16K / 2.7 GB	180	0
...c Deepseek Coder 1.3B Instruct	16K / 5.4 GB	5	0
Hpc Coder V2.1.3B	16K / 2.7 GB	15	5
Pip Api Expert	16K / 5.4 GB	5	3
... 1.3B Instruct Trt Int4 G64 Hf	16K / 0.9 GB	7	0
Pip Library Etl 1.3B	16K / 5.4 GB	31	44
Pip Sql 1.3B	16K / 5.4 GB	91	90
...t Structured Output Peft Merge	16K / 2.7 GB	12	0
Datascience Coder 1.3B	16K / 2.7 GB	6	1

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