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Smol Llama 220M Open Instruct by BEE-spoke-data

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  Autotrain compatible Base model:bee-spoke-data/smol... Base model:finetune:bee-spoke-...   Dataset:vmware/open-instruct   Endpoints compatible   Instruct   Llama   Model-index   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/BEE-spoke-data/smol_llama-220M-open_instruct 

Smol Llama 220M Open Instruct Benchmarks

ARC: 25 vs 96.7 (so35)-74.1%
HellaSwag: 29.71 vs 95.3 (gpt4)-68.8%
MMLU: 26.11 vs 88.3 (so35)-70.4%
TruthfulQA: 44.06 vs 59 (gpt4)-25.3%
WinoGrande: 50.28 vs 87.5 (gpt4)-42.5%
LLME Score: 0.21294
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 Open Instruct (BEE-spoke-data/smol_llama-220M-open_instruct)

Smol Llama 220M Open Instruct Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model inference API parameters are set to sample with low temperature to ensure some variation in generations. Feel free to experiment with parameter settings.
Training Details 
Data Sources:
VMware/open-instruct
Input Output 
Input Format:
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
Output Format:
Responses to the instruction.
Performance Tips:
Experiment with inference parameters for improved performance.
LLM NameSmol Llama 220M Open Instruct
Repository 🤗https://huggingface.co/BEE-spoke-data/smol_llama-220M-open_instruct 
Base Model(s)  Smol Llama 220M GQA   BEE-spoke-data/smol_llama-220M-GQA
Model Size220m
Required VRAM0.4 GB
Updated2024-12-21
MaintainerBEE-spoke-data
Model Typellama
Instruction-BasedYes
Model Files  0.4 GB   0.0 GB
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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Rank the Smol Llama 220M Open Instruct 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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