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Smol Llama 220M Bees Internal by BEE-spoke-data

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Smol Llama 220M Bees Internal is an open-source language model by BEE-spoke-data. Features: 220m LLM, VRAM: 0.4GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.11.

Base model:bee-spoke-data/smol... Base model:finetune:bee-spoke-... Dataset:bee-spoke-data/bees-in...   En   Endpoints compatible   Generated from trainer   Llama   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/BEE-spoke-data/smol_llama-220M-bees-internal 

Smol Llama 220M Bees Internal Benchmarks

LLME Score: 0.11288
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Smol Llama 220M Bees Internal (BEE-spoke-data/smol_llama-220M-bees-internal)
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Smol Llama 220M Bees Internal Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model fine-tuning involves various usage of hyperparameters such as a learning rate of 0.0001, training batch size of 4, and utilizes Adam Optimizer with specific betas (0.9,0.95) and epsilon (1e-08). Training proceeded with gradients accumulated every 8 steps, using a cosine learning rate scheduler with warmup ratio 0.05 for 2 epochs.
Supported Languages 
en (high proficiency)
Training Details 
Data Sources:
BEE-spoke-data/bees-internal
LLM NameSmol Llama 220M Bees Internal
Repository 🤗https://huggingface.co/BEE-spoke-data/smol_llama-220M-bees-internal 
Base Model(s)  Smol Llama 220M GQA   BEE-spoke-data/smol_llama-220M-GQA
Model Size220m
Required VRAM0.4 GB
Updated2026-03-29
MaintainerBEE-spoke-data
Model Typellama
Model Files  0.4 GB   0.0 GB
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32128
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than BEE-spoke-data/smol_llama-220M-bees-internal.

Rank the Smol Llama 220M Bees Internal 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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