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Datascience Coder 6.7B by ed001

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  Autotrain compatible   Code   Codegen   Conversational   Data science Dataset:ed001/ds-coder-instruc...   En   Endpoints compatible   Instruct   Llama   Model-index   Pytorch   Region:us   Sharded

Datascience Coder 6.7B Benchmarks

ARC: 34.64 vs 96.7 (so35)-64.2%
HellaSwag: 53.83 vs 95.3 (gpt4)-43.5%
MMLU: 37.96 vs 88.3 (so35)-57%
TruthfulQA: 44.82 vs 59 (gpt4)-24%
WinoGrande: 55.72 vs 87.5 (gpt4)-36.3%
GSM8K: 24.94 vs 96.4 (so35)-74.1%
LLME Score: 0.22331
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Datascience Coder 6.7B Parameters and Internals

Model Type 
text-generation
Additional Notes 
Trained on the Python subset of the ds-coder-instruct dataset.
Training Details 
Data Sources:
ed001/ds-coder-instruct-v1
Methodology:
fine-tuned
Context Length:
4096
Input Output 
Accepted Modalities:
text
Output Format:
text
LLM NameDatascience Coder 6.7B
Repository 🤗https://huggingface.co/ed001/datascience-coder-6.7b 
Model Size6.7b
Required VRAM13.5 GB
Updated2024-11-21
Maintainered001
Model Typellama
Instruction-BasedYes
Model Files  10.0 GB: 1-of-2   3.5 GB: 2-of-2
Supported Languagesen
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensecc-by-nc-sa-4.0
Context Length16384
Model Max Length16384
Transformers Version4.31.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<｜begin▁of▁sentence｜>
End of Sentence Token<|EOT|>
Vocabulary Size32256
Torch Data Typefloat16
Datascience Coder 6.7B (ed001/datascience-coder-6.7b)

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Note: green Score (e.g. "73.2") means that the model is better than ed001/datascience-coder-6.7b.

Rank the Datascience Coder 6.7B 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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Release v20241110
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