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Phind CodeLlama 34B V2 by Phind

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  Autotrain compatible   Code llama   Codegen   Endpoints compatible   Llama   Model-index   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2 

Phind CodeLlama 34B V2 Benchmarks

ARC: 24.57 vs 96.7 (so35)-74.6%
HellaSwag: 27.6 vs 95.3 (gpt4)-71%
MMLU: 25.76 vs 88.3 (so35)-70.8%
TruthfulQA: 48.37 vs 59 (gpt4)-18%
WinoGrande: 71.82 vs 87.5 (gpt4)-17.9%
GSM8K: 23.2 vs 96.4 (so35)-75.9%
LLME Score: 0.26292
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phind CodeLlama 34B V2 (Phind/Phind-CodeLlama-34B-v2)

Phind CodeLlama 34B V2 Parameters and Internals

Model Type 
text generation, multi-lingual
Additional Notes 
This model is instruction-tuned to be steerable and easy-to-use. More information can be found in the blog post.
Supported Languages 
Python (proficient), C/C++ (proficient), TypeScript (proficient), Java (proficient), multi-lingual (general proficiency)
Training Details 
Data Sources:
proprietary dataset of programming problems and solutions
Data Volume:
1.5B tokens
Methodology:
instruction-tuned (Alpaca/Vicuna format)
Context Length:
4096
Training Time:
15 hours
Hardware Used:
32x A100-80GB GPUs
Model Architecture:
DeepSpeed ZeRO 3 with Flash Attention 2
Input Output 
Input Format:
Alpaca/Vicuna instruction format
Accepted Modalities:
text
Output Format:
text
LLM NamePhind CodeLlama 34B V2
Repository 🤗https://huggingface.co/Phind/Phind-CodeLlama-34B-v2 
Model Size34b
Required VRAM67.5 GB
Updated2024-12-22
MaintainerPhind
Model Typellama
Model Files  9.8 GB: 1-of-7   9.7 GB: 2-of-7   9.7 GB: 3-of-7   9.7 GB: 4-of-7   9.7 GB: 5-of-7   9.7 GB: 6-of-7   9.2 GB: 7-of-7
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.33.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Phind CodeLlama 34B V2

Model
Likes
Downloads
VRAM
Phind CodeLlama 34B V2 GGUF164350614 GB
Phind Codellama 34B V2 EXL216120 GB
Phind CodeLlama 34B V2 GPTQ8926617 GB
Phind CodeLlama 34B V2 AWQ3218118 GB

Best Alternatives to Phind CodeLlama 34B V2

Best Alternatives
Context / RAM
Downloads
Likes
...gpt 32K Codellama 34B Instruct32K / 67.5 GB4812
CodeLlama 34B Instruct Hf16K / 67.5 GB45712280
ReflectionCoder CL 34B16K / 67.6 GB35720
Speechless Codellama 34B V2.016K / 67.5 GB116617
Phind CodeLlama 34B V116K / 67.5 GB966324
CodeLlama 34B Hf16K / 67.5 GB8286168
CodeLlama 34B Instruct Hf16K / 67.5 GB212214
Phind CodeLlama 34B Python V116K / 67.5 GB916253
CodeLlama 34B Python Hf16K / 67.5 GB414194
CodeLlama 34B Hf16K / 67.5 GB48612
Note: green Score (e.g. "73.2") means that the model is better than Phind/Phind-CodeLlama-34B-v2.

Rank the Phind CodeLlama 34B V2 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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