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Update InferenceAPI docs (TGI update mostly) #1432

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Sep 27, 2024
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Update InferenceAPI docs (TGI update mostly) #1432

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c243819
Update InferenceAPI docs (TGI update mostly)
Wauplin Sep 26, 2024
d7e14e3
fix script
Wauplin Sep 26, 2024
c0f853a
fix again
Wauplin Sep 26, 2024
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5 changes: 2 additions & 3 deletions docs/api-inference/tasks/automatic-speech-recognition.md
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Expand Up @@ -30,7 +30,6 @@ For more details about the `automatic-speech-recognition` task, check out its [d
### Recommended models

- [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3): A powerful ASR model by OpenAI.
- [facebook/seamless-m4t-v2-large](https://huggingface.co/facebook/seamless-m4t-v2-large): An end-to-end model that performs ASR and Speech Translation by MetaAI.
- [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1): Powerful speaker diarization model.

This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=automatic-speech-recognition&sort=trending).
Expand Down Expand Up @@ -117,9 +116,9 @@ To use the JavaScript client, see `huggingface.js`'s [package reference](https:/
| **                epsilon_cutoff** | _number_ | If set to float strictly between 0 and 1, only tokens with a conditional probability greater than epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) for more details. |
| **                eta_cutoff** | _number_ | Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) for more details. |
| **                max_length** | _integer_ | The maximum length (in tokens) of the generated text, including the input. |
| **                max_new_tokens** | _integer_ | The maximum number of tokens to generate. Takes precedence over maxLength. |
| **                max_new_tokens** | _integer_ | The maximum number of tokens to generate. Takes precedence over max_length. |
| **                min_length** | _integer_ | The minimum length (in tokens) of the generated text, including the input. |
| **                min_new_tokens** | _integer_ | The minimum number of tokens to generate. Takes precedence over maxLength. |
| **                min_new_tokens** | _integer_ | The minimum number of tokens to generate. Takes precedence over min_length. |
| **                do_sample** | _boolean_ | Whether to use sampling instead of greedy decoding when generating new tokens. |
| **                early_stopping** | _enum_ | Possible values: never, true, false. |
| **                num_beams** | _integer_ | Number of beams to use for beam search. |
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