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RichardErkhov/readomni_-_dao-9b-gguf
readomni_-_dao-9b-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
35
2% of all-time downloads
All-time downloads
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103 GB
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.gguf115 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
dao-9b - GGUF
| Name | Quant method | Size |
|---|---|---|
| dao-9b.Q2_K.gguf | Q2_K | 3.12GB |
| dao-9b.IQ3_XS.gguf | IQ3_XS | 1.94GB |
| dao-9b.IQ3_S.gguf | IQ3_S | 3.64GB |
| dao-9b.Q3_K_S.gguf | Q3_K_S | 3.63GB |
| dao-9b.IQ3_M.gguf | IQ3_M | 3.78GB |
| dao-9b.Q3_K.gguf | Q3_K | 4.03GB |
| dao-9b.Q3_K_M.gguf | Q3_K_M | 4.03GB |
| dao-9b.Q3_K_L.gguf | Q3_K_L | 4.37GB |
| dao-9b.IQ4_XS.gguf | IQ4_XS | 4.5GB |
| dao-9b.Q4_0.gguf | Q4_0 | 4.69GB |
| dao-9b.IQ4_NL.gguf | IQ4_NL | 4.73GB |
| dao-9b.Q4_K_S.gguf | Q4_K_S | 4.72GB |
| dao-9b.Q4_K.gguf | Q4_K | 4.96GB |
| dao-9b.Q4_K_M.gguf | Q4_K_M | 4.96GB |
| dao-9b.Q4_1.gguf | Q4_1 | 5.19GB |
| dao-9b.Q5_0.gguf | Q5_0 | 5.69GB |
| dao-9b.Q5_K_S.gguf | Q5_K_S | 5.69GB |
| dao-9b.Q5_K.gguf | Q5_K | 5.83GB |
| dao-9b.Q5_K_M.gguf | Q5_K_M | 5.83GB |
| dao-9b.Q5_1.gguf | Q5_1 | 6.19GB |
| dao-9b.Q6_K.gguf | Q6_K | 6.75GB |
| dao-9b.Q8_0.gguf | Q8_0 | 8.74GB |
license: apache-2.0 base_model: 01-ai/Yi-1.5-9B model-index:
Dao-9B is the smaller, open-source version of the translation model powering the Omni Translator, a state-of-the-art literary translation tool. While less capable than the full model, as it's on a less recent methodology and data, Dao-9B is still a powerful translation model that can be run locally and performs especially well on Chinese webnovels.
While no comparison is available for this model, you can find the comparison page (against several other translation tools) for the full model here.
To get started quickly, you can explore the starter Colab notebook here.
We do not provide inference service for this model, you can instead try out the more powerful full model on the Omni Translator.
To fully utilize the capability of this model in translation, it is recommended to follow the steps below:
\n\n is used to separate paragraphs. Normalize the text via Unicode normalization (NFKC) and remove any extra spaces.We provide a starting code that demonstrates most of the above steps in the starter Colab notebook.
Use the following prompt template to extract terms from the text:
<context>
{context}
</context>
<passage>
{input}
</passage>
Given the above passage, please list out the terminologies and namings present that may be reused in the translations of future passages. Output the terminologies in the format of Raw, English. Use the CSV format.
Use the following prompt template to perform translation:
<context>
{context}
</context>
<passage>
{input}
</passage>
<terms>
{terms}
</terms>
You are a professional translator. Given the above passage, please translate the passage to English.