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Phillama 3.8B V0.1 by raincandy-u

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Phillama 3.8B V0.1 is an open-source language model by raincandy-u. Features: 3.8b LLM, VRAM: 7.6GB, Context: 4K, License: mit, Instruction-Based, LLM Explorer Score: 0.14, Arc: 62.5, HellaSwag: 78.2, MMLU: 67.3, GSM8K: 72.6.

  Conversational Dataset:raincandy-u/dextrometh...   Endpoints compatible   Instruct   Llama   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/raincandy-u/phillama-3.8b-v0.1 

Phillama 3.8B V0.1 Benchmarks

ARC: 62.46 vs 96.7 (so35)-35.4%
HellaSwag: 78.2 vs 95.3 (gpt4)-17.9%
MMLU: 67.27 vs 88.3 (so35)-23.8%
GSM8K: 72.55 vs 96.4 (so35)-24.7%
LLME Score: 0.14004
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phillama 3.8B V0.1 (raincandy-u/phillama-3.8b-v0.1)
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Phillama 3.8B V0.1 Parameters and Internals

Model Type 
text-generation
Additional Notes 
Converted into Llama format for compatibility with Llama-2/3 workflow.
Training Details 
Data Sources:
raincandy-u/Dextromethorphan-10k
Methodology:
Trained on Llama-generated dataset to make it more "llama-like".
Input Output 
Input Format:
<|system|> You are a humanoid AI assistant. You think step by step and give detailed long response.<|end|> <|user|> Example Question<|end|>
Accepted Modalities:
text
LLM NamePhillama 3.8B V0.1
Repository 🤗https://huggingface.co/raincandy-u/phillama-3.8b-v0.1 
Model Size3.8b
Required VRAM7.6 GB
Updated2026-05-23
Maintainerraincandy-u
Model Typellama
Instruction-BasedYes
Model Files  7.6 GB
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.40.0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than raincandy-u/phillama-3.8b-v0.1.

Rank the Phillama 3.8B 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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