What are the hardware requirements for Smol Llama 101M GQA Python?

Smol Llama 101M GQA Python requires approximately 0.4 GB of VRAM and supports a context window of 1K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Smol Llama 101M GQA Python and how large is it?

Smol Llama 101M GQA Python is developed by BEE-spoke-data, a model with 101m 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 Smol Llama 101M GQA Python?

Smol Llama 101M GQA Python is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Smol Llama 101M GQA Python by BEE-spoke-data — VRAM 0.4GB, 1K context

Name: Smol Llama 101M GQA Python
Author: BEE-spoke-data

Smol Llama 101M GQA Python is an open-source language model by BEE-spoke-data. Features: 101m LLM, VRAM: 0.4GB, Context: 1K, License: apache-2.0, LLM Explorer Score: 0.09.

Codegen Dataset:bee-spoke-data/pypi cl... En Endpoints compatible Llama Markdown Python Region:us Safetensors Smol llama

Model Card on HF 🤗: https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python

Smol Llama 101M GQA Python Parameters and Internals

Model Type

code generation, text generation

Use Cases

Areas:

research, education

Applications:

Python code generation, Markdown generation

Primary Use Cases:

generating basic Python functions, creating simple scripting utilities

Limitations:

struggles with complex reasoning and planning tasks

Considerations:

Use with understanding that it may contain bugs and is best suited for educational or experimental purposes.

Additional Notes

The model is part of an experiment to explore code generation capabilities of smaller models. May create useful utilities but is not optimized for more complex tasks.

Supported Languages

en (basic level)

Training Details

Data Sources:

BEE-spoke-data/pypi_clean-deduped

Methodology:

general pre-trained checkpoint with additional Python-related tokens added to vocab

Training Time:

+1 epoch

Model Architecture:

similar to base model architecture with new tokens

Input Output

Input Format:

text prompts, specifically Python code snippets or descriptions

Accepted Modalities:

text

Output Format:

generated code snippets in Python or README markdown

Performance Tips:

May require specific configurations in tokenizer settings (e.g., use_fast=False).

LLM Name	Smol Llama 101M GQA Python
Repository 🤗	https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python
Model Size	101m
Required VRAM	0.4 GB
Updated	2026-08-24
Maintainer	BEE-spoke-data
Model Type	llama
Model Files	0.4 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	1024
Model Max Length	1024
Transformers Version	4.33.3
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32128
Torch Data Type	float32

Rank the Smol Llama 101M GQA Python 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 Smol Llama 101M GQA Python

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Smol Llama 101M GQA	1K / 0.4 GB	465	33
Smol Llama 101M Chat V1	1K / 0.4 GB	85	9
...ephyr Smol Llama 100M Sft Full	1K / 0.2 GB	97	1
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Note: green Score (e.g. "73.2") means that the model is better than BEE-spoke-data/smol_llama-101M-GQA-python.