Duplicate from rAIfle/SorcererLM-8x22b-bf16
Browse filesCo-authored-by: rAIfle <[email protected]>
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- README.md +42 -0
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README.md
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---
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license: apache-2.0
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base_model: alpindale/WizardLM-2-8x22B
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---
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# SorcererLM-8x22b-bf16
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<img src="https://files.catbox.moe/1kohx8.png" width="400"/>
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<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/6569a4ed2419be6072890cf8/L_uGojVkNUsK6QHvWgs9o.mpga"></audio>
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Oh boy, here we go. Low-rank (`r=16, alpha=32`) 16bit-LoRA on top of [WizardLM-2-8x22B](https://huggingface.co/alpindale/WizardLM-2-8x22B), trained on 2 epochs of (cleaned & deduped) c2-logs. As far as I can tell, this is an upgrade from `WizardLM-2-8x22B` for RP purposes.
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Alongside this ready-to-use release I'm also releasing [the LoRA itself](https://huggingface.co/rAIfle/SorcererLM-8x22b-epoch2-LoRA) as well as [the earlier `epoch1`-checkpoint of the LoRA](https://huggingface.co/rAIfle/SorcererLM-8x22b-epoch1-LoRA).
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## Why A LoRA?
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The choice was fully intentional. I briefly considered a FFT but for this particular use-case a LoRA seemed a better fit. `WizardLM-2-8x22B` is smart by itself but its used vocabulary leaves much to be desired when it comes to RP. By training a low-rank LoRA on top of it to teach it some of Claude's writing style, we remedy that.
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## Prompting
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- Use the templates in [Quant-Cartel/Recommended-Settings](https://huggingface.co/Quant-Cartel/Recommended-Settings) under the `SorcererLM`-folder.
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- Or Vicuna 1.1 and a sane context template. It's somewhat sensitive to samplers, I'd recommend Temperature 1, MinP 0.05 and a dash of DRY but YMMV. Shorter prompts seem to work better, too.
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## Quantized Versions
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- [iMat GGUFs](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-iMat-GGUF)
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- [longcal exl2s](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-exl2-longcal)
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## Acknowledgments
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The main shoutout I want to make is to my [Cartel](https://huggingface.co/Quant-Cartel) bros, [Envoid](https://huggingface.co/Envoid) and particularly [I^2](https://huggingface.co/InferenceIllusionist), for being amazing. I count this as a team effort, so they deserve kudos too if you like this.
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## Training
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Trained using [qlora-pipe](https://github.com/tdrussell/qlora-pipe). Configs included in the `train`-subfolder.
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## Safety
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... n/a
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config.json
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{
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"_name_or_path": "alpindale/WizardLM-2-8x22B",
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"architectures": [
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"MixtralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 6144,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 65536,
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"model_type": "mixtral",
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"num_attention_heads": 48,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 56,
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"num_key_value_heads": 8,
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"num_local_experts": 8,
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"output_router_logits": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000,
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"router_aux_loss_coef": 0.001,
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"router_jitter_noise": 0.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.45.0.dev0",
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"use_cache": false,
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"vocab_size": 32000
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}
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