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Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF to start chatting
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# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF to start chatting
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QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF

This is quantized version of OwenArli/ArliAI-Llama-3-8B-Dolfin-v0.5 created using llama.cpp

Model Description

Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

This is a fine tune using an improved Dolphin and WizardLM dataset intended to make the model follow instructions better and refuse less.

OpenLLM Benchmark:

Training:

  • 2048 sequence length since the dataset has an average length of under 1000 tokens, while the base model is 8192 sequence length. From testing it still performs the same 8192 context just fine.
  • Training duration is around 2 days on 2xRTX 3090, using 4-bit loading and Qlora 64-rank 128-alpha resulting in ~2% trainable weights.

Instruct format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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GGUF
Model size
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Architecture
llama
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