Quantized for CPU
Collection
quantized models that will run on almost anything
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4 items
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Updated
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Model Card for Model mistral-trimegistus-7b-gguf
This model repo holds gguf quantized versions of ["teknium/Mistral-Trismegistus-7B"] (https://huggingface.co/teknium/Mistral-Trismegistus-7B).
Model Details
Transcendence is All You Need! Mistral Trismegistus is a model made for people interested in the esoteric, occult, and spiritual.
Model Description
The First Powerful Occult Expert Model: ~10,000 high quality, deep, rich, instructions on the occult, esoteric, and spiritual.
Fast: Trained on Mistral, a state of the art 7B parameter model, you can run this model FAST on even a cpu.
Not a positivity-nazi: This model was trained on all forms of esoteric tasks and knowledge, and is not burdened by the flowery nature of many other models, who chose positivity over creativity.
Model Sources [optional]
All credits go here
Usage
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Training Hyperparameters
"_name_or_path": { "desc": null, "value": "mistralai/Mistral-7B-v0.1" }, "architectures": { "desc": null, "value": [ "MistralForCausalLM" ] }, "bad_words_ids": { "desc": null, "value": null }, "bench_dataset": { "desc": null, "value": "pharaouk/dharma-1/dharma_1_mini.json" }, "learning_rate": { "desc": null, "value": 0.0004 }, "max_grad_norm": { "desc": null, "value": 1 }, "fp16_opt_level": { "desc": null, "value": "O1" }, "length_penalty": { "desc": null, "value": 1 }, "max_seq_length": { "desc": null, "value": 4096 }, "sliding_window": { "desc": null, "value": 4096 }, "num_beam_groups": { "desc": null, "value": 1 }, "initializer_range": { "desc": null, "value": 0.02 }, "intermediate_size": { "desc": null, "value": 14336 }, "lr_scheduler_type": { "desc": null, "value": "cosine" }, "num_hidden_layers": { "desc": null, "value": 32 }, "repetition_penalty": { "desc": null, "value": 1 }, "evaluation_strategy": { "desc": null, "value": "steps" }, "num_attention_heads": { "desc": null, "value": 32 }, "num_key_value_heads": { "desc": null, "value": 8 }, "quantization_config": { "desc": null, "value": { "load_in_4bit": true, "load_in_8bit": false, "quant_method": "QuantizationMethod.BITS_AND_BYTES", "llm_int8_threshold": 6, "bnb_4bit_quant_type": "nf4", "llm_int8_skip_modules": null, "bnb_4bit_compute_dtype": "bfloat16", "llm_int8_has_fp16_weight": false, "bnb_4bit_use_double_quant": true, "llm_int8_enable_fp32_cpu_offload": false } }
Speeds, Sizes, Times
{ "_step": 9589, "_wandb.runtime": 12960, "_runtime": 12960.192620515823, "eval/loss": 1.4308836460113523, "train/train_steps_per_second": 0.739, "train/train_samples_per_second": 2.956, "train/loss": 0.3396, "train/epoch": 4, "train/total_flos": 1757020072120942600, "train/train_loss": 0.8929485179171377, "train/learning_rate": 0, "eval/steps_per_second": 2.196, "_timestamp": 1696542775.2713604, "eval/runtime": 11.3829, "train/global_step": 9584, "train/train_runtime": 12962.7813, "eval/samples_per_second": 8.522 }
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