Update docs

docs/03 ‐ Parameters Tab.md

@@ -33,6 +33,7 @@ For more information about the parameters, the [transformers documentation](http
 * **max_new_tokens**: Maximum number of tokens to generate. Don't set it higher than necessary: it is used in the truncation calculation through the formula `(prompt_length) = min(truncation_length - max_new_tokens, prompt_length)`, so your prompt will get truncated if you set it too high.
 * **temperature**: Primary factor to control the randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.
 * **top_p**: If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
+* **min_p**: Tokens with probability smaller than `(min_p) * (probability of the most likely token)` are discarded. This is the same as top_a but without squaring the probability.
 * **top_k**: Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
 * **repetition_penalty**: Penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
 * **presence_penalty**: Similar to repetition_penalty, but with an additive offset on the raw token scores instead of a multiplicative factor. It may generate better results. 0 means no penalty, higher value = less repetition, lower value = more repetition. Previously called "additive_repetition_penalty".
@@ -48,7 +49,8 @@ For more information about the parameters, the [transformers documentation](http
 * **penalty_alpha**: Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.
 * **mirostat_mode**: Activates the Mirostat sampling technique. It aims to control perplexity during sampling. See the [paper](https://arxiv.org/abs/2007.14966).
 * **mirostat_tau**: No idea, see the paper for details. According to the Preset Arena, 8 is a good value. 
-* **mirostat_tau**: No idea, see the paper for details. According to the Preset Arena, 0.1 is a good value.
+* **mirostat_eta**: No idea, see the paper for details. According to the Preset Arena, 0.1 is a good value.
+* **temperature_last**: Makes temperature the last sampler instead of the first. With this, you can low probability tokens with a sampler like min_p and then use a high temperature to make the model creative without losing coherency.
 * **do_sample**: When unchecked, sampling is entirely disabled, and greedy decoding is used instead (the most likely token is always picked).
 * **Seed**: Set the Pytorch seed to this number. Note that some loaders do not use Pytorch (notably llama.cpp), and others are not deterministic (notably ExLlama v1 and v2). For these loaders, the seed has no effect.
 * **encoder_repetition_penalty**: Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.

docs/04 ‐ Model Tab.md

@@ -29,6 +29,7 @@ Options:
 * **load-in-4bit**: Load the model in 4-bit precision using bitsandbytes.
 * **trust-remote-code**: Some models use custom Python code to load the model or the tokenizer. For such models, this option needs to be set. It doesn't download any remote content: all it does is execute the .py files that get downloaded with the model. Those files can potentially include malicious code; I have never seen it happen, but it is in principle possible.
 * **use_fast**: Use the "fast" version of the tokenizer. Especially useful for Llama models, which originally had a "slow" tokenizer that received an update. If your local files are in the old "slow" format, checking this option may trigger a conversion that takes several minutes. The fast tokenizer is mostly useful if you are generating 50+ tokens/second using ExLlama_HF or if you are tokenizing a huge dataset for training.
+* **use_flash_attention_2**: Set use_flash_attention_2=True while loading the model. Possibly useful for training.
 * **disable_exllama**: Only applies when you are loading a GPTQ model through the transformers loader. It needs to be checked if you intend to train LoRAs with the model.
 
 ### ExLlama_HF
@@ -42,6 +43,8 @@ ExLlama_HF is the v1 of ExLlama (https://github.com/turboderp/exllama) connected
 * **gpu-split**: If you have multiple GPUs, the amount of memory to allocate per GPU should be set in this field. Make sure to set a lower value for the first GPU, as that's where the cache is allocated.
 * **max_seq_len**: The maximum sequence length for the model. In ExLlama, the cache is preallocated, so the higher this value, the higher the VRAM. It is automatically set to the maximum sequence length for the model based on its metadata, but you may need to lower this value be able to fit the model into your GPU. After loading the model, the "Truncate the prompt up to this length" parameter under "Parameters" > "Generation" is automatically set to your chosen "max_seq_len" so that you don't have to set the same thing twice.
 * **cfg-cache**: Creates a second cache to hold the CFG negative prompts. You need to set this if and only if you intend to use CFG in the "Parameters" > "Generation" tab. Checking this parameter doubles the cache VRAM usage.
+* **no_flash_attn**: Disables flash attention. Otherwise, it is automatically used as long as the library is installed.
+* **cache_8bit**: Create a 8-bit precision cache instead of a 16-bit one. This saves VRAM but increases perplexity (I don't know by how much).
 
 ### ExLlamav2_HF