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Browse files- LICENSE +0 -0
- README.md +54 -0
- config.json +29 -0
- mergekit_config.yml +8 -0
- model.safetensors.index.json +1 -0
- output-00001-of-00003.safetensors +3 -0
- output-00002-of-00003.safetensors +3 -0
- output-00003-of-00003.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
LICENSE
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README.md
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---
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base_model: []
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/Tn9MBg6.png" alt="MidnightMiqu" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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# Midnight-Miqu-70B-v1.5 - EXL2 2.75bpw
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This is a 2.75bpw EXL2 quant of [sophosympatheia/Midnight-Miqu-70B-v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5)
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Details about the model and the merge info can be found at the above mode page.
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I have not extensively tested this quant/model other than ensuring I could load it and chat with it.
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## Quant Details
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This is the script used for quantization.
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```bash
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#!/bin/bash
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# Activate the conda environment
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source ~/miniconda3/etc/profile.d/conda.sh
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conda activate exllamav2
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# Define variables
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MODEL_DIR="models/Midnight-Miqu-70B-v1.5"
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OUTPUT_DIR="exl2_midnightv15-70b"
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MEASUREMENT_FILE="measurements/midnight70b-v15.json"
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BIT_PRECISIONS=(6.0 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25)
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for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
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do
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CONVERTED_FOLDER="models/Midnight-Miqu-70B-v1.5_exl2_${BIT_PRECISION}bpw"
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if [ -d "$CONVERTED_FOLDER" ]; then
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echo "Skipping $BIT_PRECISION as $CONVERTED_FOLDER already exists."
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continue
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fi
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rm -r "$OUTPUT_DIR"
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mkdir "$OUTPUT_DIR"
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mkdir "$CONVERTED_FOLDER"
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python convert.py -i "$MODEL_DIR" -o "$OUTPUT_DIR" -nr -m "$MEASUREMENT_FILE" -b "$BIT_PRECISION" -cf "$CONVERTED_FOLDER"
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done```
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config.json
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{
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"_name_or_path": "midnight-miqu-70b-v1.5",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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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": 8192,
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"initializer_range": 0.02,
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"intermediate_size": 28672,
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"max_position_embeddings": 32764,
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"model_type": "llama",
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"num_attention_heads": 64,
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"num_hidden_layers": 80,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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mergekit_config.yml
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merge_method: dare_linear
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base_model: /home/llm/mergequant/models/BASE/152334H_miqu-1-70b-sf # base model
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models:
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- model: /home/llm/mergequant/models/midnight-miqu-70b-v1.0 # your merge goes here
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- model: /home/llm/mergequant/models/BASE/Tess-70B-v1.6 # new "base model" goes here
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parameters:
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weight: 1.0
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dtype: float16
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model.safetensors.index.json
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38 |
+
"spaces_between_special_tokens": false,
|
39 |
+
"tokenizer_class": "LlamaTokenizer",
|
40 |
+
"unk_token": "<unk>",
|
41 |
+
"use_default_system_prompt": false
|
42 |
+
}
|