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add2ac4
Update README.md
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README.md
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@@ -51,6 +51,68 @@ I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" wh
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Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
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24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). Empty 16GB GPUs can still run the high context with aggressive quantization.
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To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.
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Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
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@MarinaraSpaghetti has extensively tested the model and recommended the following settings, and they seem to work quite well:
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```
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{
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"temp": 1,
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"temperature_last": true,
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"top_p": 1,
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"top_k": 0,
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"top_a": 0,
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"tfs": 1,
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"epsilon_cutoff": 0,
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"eta_cutoff": 0,
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"typical_p": 0.9,
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"min_p": 0,
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"rep_pen": 1.1,
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"rep_pen_range": 19456,
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"no_repeat_ngram_size": 0,
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"penalty_alpha": 0,
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"num_beams": 1,
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"length_penalty": 0,
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"min_length": 0,
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"encoder_rep_pen": 1,
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"freq_pen": 0,
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"presence_pen": 0,
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"do_sample": true,
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"early_stopping": false,
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"dynatemp": false,
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"min_temp": 1,
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"max_temp": 2,
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"dynatemp_exponent": 1,
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"smoothing_factor": 0.33,
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"add_bos_token": false,
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"truncation_length": 2048,
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"ban_eos_token": false,
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"skip_special_tokens": true,
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"streaming": true,
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"mirostat_mode": 0,
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"mirostat_tau": 5,
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"mirostat_eta": 0.1,
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"guidance_scale": 1,
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"negative_prompt": "",
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"grammar_string": "",
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"banned_tokens": "",
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"ignore_eos_token_aphrodite": false,
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"spaces_between_special_tokens_aphrodite": true,
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"sampler_order": [
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6,
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0,
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],
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"logit_bias": [],
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"n": 1,
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"rep_pen_size": 0,
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"genamt": 400,
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"max_length": 38912
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}
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```
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24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). Empty 16GB GPUs can still run the high context with aggressive quantization.
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To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.
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