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OwenArli_-_ArliAI-Llama-3-8…/README.md
ModelHub XC 1bf6bdb169 初始化项目,由ModelHub XC社区提供模型
Model: RichardErkhov/OwenArli_-_ArliAI-Llama-3-8B-Dolfin-v0.3-gguf
Source: Original Platform
2026-10-30 22:56:19 +08:00

5.9 KiB

Quantization made by Richard Erkhov.

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ArliAI-Llama-3-8B-Dolfin-v0.3 - GGUF

Name Quant method Size
ArliAI-Llama-3-8B-Dolfin-v0.3.Q2_K.gguf Q2_K 2.96GB
ArliAI-Llama-3-8B-Dolfin-v0.3.IQ3_XS.gguf IQ3_XS 3.28GB
ArliAI-Llama-3-8B-Dolfin-v0.3.IQ3_S.gguf IQ3_S 3.43GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q3_K_S.gguf Q3_K_S 3.41GB
ArliAI-Llama-3-8B-Dolfin-v0.3.IQ3_M.gguf IQ3_M 3.52GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q3_K.gguf Q3_K 3.74GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q3_K_M.gguf Q3_K_M 3.74GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q3_K_L.gguf Q3_K_L 4.03GB
ArliAI-Llama-3-8B-Dolfin-v0.3.IQ4_XS.gguf IQ4_XS 4.18GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q4_0.gguf Q4_0 4.34GB
ArliAI-Llama-3-8B-Dolfin-v0.3.IQ4_NL.gguf IQ4_NL 4.38GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q4_K_S.gguf Q4_K_S 4.37GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q4_K.gguf Q4_K 4.58GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q4_K_M.gguf Q4_K_M 4.58GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q4_1.gguf Q4_1 4.78GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q5_0.gguf Q5_0 5.21GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q5_K_S.gguf Q5_K_S 5.21GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q5_K.gguf Q5_K 5.34GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q5_K_M.gguf Q5_K_M 5.34GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q5_1.gguf Q5_1 5.65GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q6_K.gguf Q6_K 6.14GB
ArliAI-Llama-3-8B-Dolfin-v0.3.Q8_0.gguf Q8_0 7.95GB

Original model description:

license: llama3

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 is running...

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 1 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|>

Quants:

FP16: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Dolfin-v0.3

GGUF: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Dolfin-v0.3-GGUF