move to new file, add in different inference runs

Author: dsikka

src/deepsparse/transformers/README.md

@@ -355,73 +355,6 @@ To learn more about benchmarking, refer to the appropriate documentation.
 Also, check out our [Benchmarking tutorial](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/benchmark)!
 
 
-<h1><summary>Text Generation GenerationConfig Features Supported </h1></summary>
-
-<details>
-<h2> Parameters controlling the output length: </h2>
-
-| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
-| :---    |      :----: |         :----:     |        :----:       |       ---:|
-| max_length | Maximum length of generated tokens. Equal to input_prompt + max_new_tokens. Overridden by max_new_tokens | 1024 | 20 | Yes|
-| max_new_tokens | Maximum number of tokens to generate, ignoring prompt tokens. | None | None | Yes |
-| min_length | Minimum length of generated tokens. Equal to input_prompt + min_new_tokens. Overridden by min_new_tokens | - | 0 | No
-| min_new_tokens | Minomum number of tokens to generate, ignoring prompt tokens. | - | None | No |
-| max_time | - | - | - | No |
-
-<br/>
-<h2> Parameters for manipulation of the model output logits </h2>
-
-| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
-| :---    |      :----: |         :----:     |        :----:       |       ---:|
-| top_k | The number of highest probability vocabulary tokens to keep for top-k-filtering | 0 | 50 | Yes
-| top_p | Keep the generated tokens where its cumulative probability is >= top_p | 0.0 | 1.0 | Yes
-| repetition_penalty | Penalty applied for generating new token. Existing token frequencies summed to subtraction the logit of its corresponding logit value | 0.0 | 1.0 | Yes |
-| temperature | The temperature to use when sampling from the probability distribution computed from the logits. Higher values will result in more random samples. Should be greater than 0.0 | 1.0 | 1.0 | Yes |
-| typical_p | - | - | - | No |
-| epsilon_cutoff | - | - | - | No |
-| eta_cutoff | - | - | - | No |
-| diversity_penalty | - | - | - | No |
-| length_penalty | - | - | - | No |
-| bad_words_ids | - | - | - | No |
-| force_words_ids | - | - | - | No |
-| renormalize_logits | - | - | - | No |
-| constraints | - | - | - | No |
-| forced_bos_token_id | - | - | - | No |
-| forced_eos_token_id | - | - | - | No |
-| remove_invalid_values | - | - | - | No |
-| exponential_decay_length_penalty | - | - | - | No |
-| suppress_tokens | - | - | - | No |
-| begin_suppress_tokens | - | - | - | No |
-| forced_decoder_ids | - | - | - | No |
-
-<br/>
-<h2> Parameters that control the generation strategy used </h2>
-
-| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
-| :---    |      :----: |         :----:     |        :----:       |       ---:|
-| do_sample | If True, will apply sampling from the probability distribution computed from the logits | False | False | Yes |
-
-<br/>
-<h2> Parameters for output variables: </h2>
-
-| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
-| :---    |      :----: |         :----:     |        :----:       |       ---:|
-| num_return_sequences | The number of sequences generated for each prompt | 1 | 1 | Yes |
-| output_scores | Whether to return the generated logits | False | False | Yes |
-| return_dict_generate | - | - | - | No |
-
-<br/>
-<h2> Special Tokens: </h2>
-
-| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
-| :---    |      :----: |         :----:     |        :----:       |       ---:|
-| pad_token_id | - | - | - | No |
-| bos_token_id | - | - | - | No |
-| eos_token_id | - | - | - | No |
-
-</details>
-<br/>
-
 ## Tutorials:
 For a deeper dive into using transformers within the Neural Magic ecosystem, refer to the detailed tutorials on our [website](https://neuralmagic.com/):
 - [Token Classification: Named Entity Recognition](https://neuralmagic.com/use-cases/sparse-named-entity-recognition/)