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Coma 7B V0.1 by DevQuasar-5

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Coma 7B V0.1 is an open-source language model by DevQuasar-5. Features: 7b LLM, VRAM: 13.6GB, Context: 4K, License: llama2, Code Generating, Merged, LLM Explorer Score: 0.14.

  Merged Model   Arxiv:2203.05482 Base model:meta-llama/codellam... Base model:meta-llama/llama-2-...   Codegen   Endpoints compatible   Llama   Model-index   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/DevQuasar-5/coma-7B-v0.1 

Coma 7B V0.1 Benchmarks

LLME Score: 0.13523
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Coma 7B V0.1 (DevQuasar-5/coma-7B-v0.1)
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Coma 7B V0.1 Parameters and Internals

LLM NameComa 7B V0.1
Repository 🤗https://huggingface.co/DevQuasar-5/coma-7B-v0.1 
Base Model(s)  Llama 2 7B Hf   CodeLlama 7B Hf   meta-llama/Llama-2-7b-hf   meta-llama/CodeLlama-7b-hf
Merged ModelYes
Model Size7b
Required VRAM13.6 GB
Updated2026-05-19
MaintainerDevQuasar-5
Model Typellama
Model Files  0.9 GB: 1-of-14   1.0 GB: 2-of-14   1.0 GB: 3-of-14   1.0 GB: 4-of-14   1.0 GB: 5-of-14   0.9 GB: 6-of-14   1.0 GB: 7-of-14   1.0 GB: 8-of-14   1.0 GB: 9-of-14   1.0 GB: 10-of-14   0.9 GB: 11-of-14   1.0 GB: 12-of-14   1.0 GB: 13-of-14   0.9 GB: 14-of-14
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.41.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

Best Alternatives to Coma 7B V0.1

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Deepseek Coder 6.7B Instruct16K / 13.5 GB150309493
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CodeLlama 7B Instruct Hf16K / 13.5 GB45380255
GetCode Slerp16K / 13.6 GB1601
Wizardllama 7B16K / 13.5 GB1110
Codellama 7B Hf ReFT GSM8k16K / 13.5 GB9161
CodeLlama 7B Python Hf16K / 13.5 GB3028144
MathCoder2 CodeLlama 7B16K / 13.5 GB65
GoLLIE 7B16K / 40.5 GB207030
Stack Codellama 7B Inst16K / 13.5 GB870
Note: green Score (e.g. "73.2") means that the model is better than DevQuasar-5/coma-7B-v0.1.

Rank the Coma 7B V0.1 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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