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Document logits_all

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README.md
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@ -327,6 +327,7 @@ Optionally, you can use the following command-line flags:
| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. | | `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. |
| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default is 0 (random). | | `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default is 0 (random). |
| `--numa` | Activate NUMA task allocation for llama.cpp. | | `--numa` | Activate NUMA task allocation for llama.cpp. |
| `--logits_all`| Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower. |
| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. | | `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
#### ExLlama #### ExLlama

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docs/04 ‐ Model Tab.md
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@ -110,6 +110,10 @@ To use it, you need to download a tokenizer. There are two options:
1) Download `oobabooga/llama-tokenizer` under "Download model or LoRA". That's a default Llama tokenizer. 1) Download `oobabooga/llama-tokenizer` under "Download model or LoRA". That's a default Llama tokenizer.
2) Place your .gguf in a subfolder of `models/` along with these 3 files: `tokenizer.model`, `tokenizer_config.json`, and `special_tokens_map.json`. This takes precedence over Option 1. 2) Place your .gguf in a subfolder of `models/` along with these 3 files: `tokenizer.model`, `tokenizer_config.json`, and `special_tokens_map.json`. This takes precedence over Option 1.
It has an additional parameter:
* **logits_all**: Needs to be checked if you want to evaluate the perplexity of the llama.cpp model using the "Training" > "Perplexity evaluation" tab. Otherwise, leave it unchecked, as it makes prompt processing slower.
### ctransformers ### ctransformers
Loads: GGUF/GGML models. Loads: GGUF/GGML models.