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5a6bc6b1a6
* model-conversion: add model card template for embeddings [no ci] This commit adds a separate model card template (model repository README.md template) for embedding models. The motivation for this is that there server command for the embedding model is a little different and some addition information can be useful in the model card for embedding models which might not be directly relevant for causal models. * squash! model-conversion: add model card template for embeddings [no ci] Fix pyright lint error. * remove --pooling override and clarify embd_normalize usage
49 lines
1.3 KiB
Plaintext
49 lines
1.3 KiB
Plaintext
---
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base_model:
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- {base_model}
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---
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# {model_name} GGUF
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Recommended way to run this model:
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```sh
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llama-server -hf {namespace}/{model_name}-GGUF
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```
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Then the endpoint can be accessed at http://localhost:8080/embedding, for
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example using `curl`:
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```console
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curl --request POST \
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--url http://localhost:8080/embedding \
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--header "Content-Type: application/json" \
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--data '{{"input": "Hello embeddings"}}' \
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--silent
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```
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Alternatively, the `llama-embedding` command line tool can be used:
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```sh
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llama-embedding -hf {namespace}/{model_name}-GGUF --verbose-prompt -p "Hello embeddings"
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```
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#### embd_normalize
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When a model uses pooling, or the pooling method is specified using `--pooling`,
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the normalization can be controlled by the `embd_normalize` parameter.
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The default value is `2` which means that the embeddings are normalized using
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the Euclidean norm (L2). Other options are:
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* -1 No normalization
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* 0 Max absolute
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* 1 Taxicab
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* 2 Euclidean/L2
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* \>2 P-Norm
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This can be passed in the request body to `llama-server`, for example:
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```sh
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--data '{{"input": "Hello embeddings", "embd_normalize": -1}}' \
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```
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And for `llama-embedding`, by passing `--embd-normalize <value>`, for example:
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```sh
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llama-embedding -hf {namespace}/{model_name}-GGUF --embd-normalize -1 -p "Hello embeddings"
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```
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