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RichardErkhov/Ba2han_-_Llama-Phi-3_DoRA-gguf
Ba2han_-_Llama-Phi-3_DoRA-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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2% of all-time downloads
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.gguf50.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Llama-Phi-3_DoRA - GGUF
| Name | Quant method | Size |
|---|---|---|
| Llama-Phi-3_DoRA.Q2_K.gguf | Q2_K | 1.35GB |
| Llama-Phi-3_DoRA.IQ3_XS.gguf | IQ3_XS | 1.49GB |
| Llama-Phi-3_DoRA.IQ3_S.gguf | IQ3_S | 1.57GB |
| Llama-Phi-3_DoRA.Q3_K_S.gguf | Q3_K_S | 1.57GB |
| Llama-Phi-3_DoRA.IQ3_M.gguf | IQ3_M | 1.65GB |
| Llama-Phi-3_DoRA.Q3_K.gguf | Q3_K | 1.75GB |
| Llama-Phi-3_DoRA.Q3_K_M.gguf | Q3_K_M | 1.75GB |
| Llama-Phi-3_DoRA.Q3_K_L.gguf | Q3_K_L | 1.9GB |
| Llama-Phi-3_DoRA.IQ4_XS.gguf | IQ4_XS | 1.93GB |
| Llama-Phi-3_DoRA.Q4_0.gguf | Q4_0 | 2.03GB |
| Llama-Phi-3_DoRA.IQ4_NL.gguf | IQ4_NL | 2.04GB |
| Llama-Phi-3_DoRA.Q4_K_S.gguf | Q4_K_S | 2.04GB |
| Llama-Phi-3_DoRA.Q4_K.gguf | Q4_K | 2.16GB |
| Llama-Phi-3_DoRA.Q4_K_M.gguf | Q4_K_M | 2.16GB |
| Llama-Phi-3_DoRA.Q4_1.gguf | Q4_1 | 2.24GB |
| Llama-Phi-3_DoRA.Q5_0.gguf | Q5_0 | 2.46GB |
| Llama-Phi-3_DoRA.Q5_K_S.gguf | Q5_K_S | 2.46GB |
| Llama-Phi-3_DoRA.Q5_K.gguf | Q5_K | 2.53GB |
| Llama-Phi-3_DoRA.Q5_K_M.gguf | Q5_K_M | 2.53GB |
| Llama-Phi-3_DoRA.Q5_1.gguf | Q5_1 | 2.68GB |
| Llama-Phi-3_DoRA.Q6_K.gguf | Q6_K | 2.92GB |
| Llama-Phi-3_DoRA.Q8_0.gguf | Q8_0 | 3.78GB |
language:
We have Llama-3 at home!
Highest PHI-3-Mini MMLU and Winogrande on the board!
The model has been trained on filtered versions of tagged datasets, as well as a few thousand more examples generated with llama-3-70B.
Use Zephyr template with any system message. Default system message should be:
You are a smart, friendly and helpful assistant.

Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 67.72 |
| AI2 Reasoning Challenge (25-Shot) | 62.29 |
| HellaSwag (10-Shot) | 79.08 |
| MMLU (5-Shot) | 69.44 |
| TruthfulQA (0-shot) | 54.08 |
| Winogrande (5-shot) | 73.40 |
| GSM8k (5-shot) | 68.01 |