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Dracones
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miqu-1-70b-sf_exl2_4.5bpw

Text Generation
Transformers
Safetensors
English
llama
exl2
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
Dracones commited on
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  1. README.md +174 -0
  2. config.json +39 -0
  3. generation_config.json +7 -0
  4. huggingface-metadata.txt +34 -0
  5. model.safetensors.index.json +730 -0
  6. output-00001-of-00005.safetensors +3 -0
  7. output-00002-of-00005.safetensors +3 -0
  8. output-00003-of-00005.safetensors +3 -0
  9. output-00004-of-00005.safetensors +3 -0
  10. output-00005-of-00005.safetensors +3 -0
  11. special_tokens_map.json +30 -0
  12. tokenizer.model +3 -0
  13. tokenizer_config.json +42 -0
README.md ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: transformers
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+ language:
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+ - en
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+ license: unknown
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+ tags:
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+ - exl2
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+ ---
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+
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+ # miqu-1-70b-sf - EXL2 4.5bpw
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+
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+ This is a 4.5bpw EXL2 quant of [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf)
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+
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+ Details about the model can be found at the above model page.
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+
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+
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+ ## EXL2 Version
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+
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+ These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library.
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+
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+ If you have problems loading these models, please update Text Generation WebUI to the latest version.
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+
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+ ## Perplexity Scoring
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+
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+ Below are the perplexity scores for the EXL2 models. A lower score is better.
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+
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+ | Quant Level | Perplexity Score |
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+ |-------------|------------------|
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+ | 5.0 | 4.2637 |
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+ | 4.5 | 4.2876 |
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+ | 4.0 | 4.3097 |
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+ | 3.5 | 4.4459 |
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+ | 3.0 | 4.6504 |
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+ | 2.75 | 5.1638 |
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+ | 2.5 | 5.1715 |
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+ | 2.25 | 6.0848 |
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+
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+ ## EQ Bench
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+
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+ Here are the EQ Bench scores for the EXL2 quants using Alpaca, ChatML, Command-R and Command-R-Plus prompt templates. A higher score is better.
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+
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+ | Quant Size | Instruct Template | Score |
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+ |------------|-------------------|-------|
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+ | 5.0 | ChatML | 79.91 |
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+ | 5.0 | Alpaca | 81.45 |
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+ | 5.0 | Mistral | 81.11 |
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+ | 5.0 | Vicuna-v1.1 | 78.37 |
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+ | 5.0 | Vicuna-v0 | 76.64 |
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+ | 4.5 | ChatML | 80.64 |
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+ | 4.5 | Alpaca | 80.9 |
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+ | 4.5 | Mistral | 81.65 |
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+ | 4.5 | Vicuna-v1.1 | 77.04 |
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+ | 4.5 | Vicuna-v0 | 74.6 |
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+ | 4.0 | ChatML | 80.78 |
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+ | 4.0 | Alpaca | 79.53 |
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+ | 4.0 | Mistral | 82.78 |
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+ | 4.0 | Vicuna-v1.1 | 79.17 |
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+ | 4.0 | Vicuna-v0 | 76.41 |
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+ | 3.5 | ChatML | 81.11 |
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+ | 3.5 | Alpaca | 82.42 |
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+ | 3.5 | Mistral | 82.34 |
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+ | 3.5 | Vicuna-v1.1 | 81.04 |
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+ | 3.5 | Vicuna-v0 | 78.09 |
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+ | 3.0 | ChatML | 79.13 |
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+ | 3.0 | Alpaca | 77.74 |
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+ | 3.0 | Mistral | 80.11 |
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+ | 3.0 | Vicuna-v1.1 | 79.38 |
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+ | 3.0 | Vicuna-v0 | 77.25 |
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+ | 2.75 | ChatML | 79.6 |
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+ | 2.75 | Alpaca | 77.85 |
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+ | 2.75 | Mistral | 79.71 |
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+ | 2.75 | Vicuna-v1.1 | 76.93 |
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+ | 2.75 | Vicuna-v0 | 75.91 |
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+ | 2.5 | ChatML | 77.45 |
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+ | 2.5 | Alpaca | 77.0 |
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+ | 2.5 | Mistral | 78.4 |
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+ | 2.5 | Vicuna-v1.1 | 75.86 |
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+ | 2.5 | Vicuna-v0 | 75.25 |
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+ | 2.25 | ChatML | 77.18 |
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+ | 2.25 | Alpaca | 74.06 |
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+ | 2.25 | Mistral | 76.75 |
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+ | 2.25 | Vicuna-v1.1 | 75.56 |
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+ | 2.25 | Vicuna-v0 | 74.28 |
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+
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+
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+
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+ ### Perplexity Script
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+
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+ This was the script used for perplexity testing.
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+
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+ ```bash
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+ #!/bin/bash
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+
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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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+
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+ # Set the model name and bit size
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+ MODEL_NAME="miqu-1-70b-sf"
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+ BIT_PRECISIONS=(8.0 7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25)
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+
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+ # Print the markdown table header
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+ echo "| Quant Level | Perplexity Score |"
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+ echo "|-------------|------------------|"
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+
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+ for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
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+ do
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+ MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
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+ if [ -d "$MODEL_DIR" ]; then
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+ output=$(python test_inference.py -m "$MODEL_DIR" -gs 22,24 -ed data/wikitext/wikitext-2-v1.parquet)
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+ score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+')
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+ echo "| $BIT_PRECISION | $score |"
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+ fi
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+ done
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+ ```
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+
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+
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+ ## Quant Details
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+
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+ This is the script used for quantization.
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+
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+ ```bash
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+ #!/bin/bash
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+
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+ # Activate the conda environment
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+ source ~/miniconda3/etc/profile.d/conda.sh
127
+ conda activate exllamav2
128
+
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+ # Set the model name and bit size
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+ MODEL_NAME="miqu-1-70b-sf"
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+
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+ # Define variables
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+ MODEL_DIR="models/152334H_miqu-1-70b-sf"
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+ OUTPUT_DIR="exl2_$MODEL_NAME"
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+ MEASUREMENT_FILE="measurements/$MODEL_NAME.json"
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+
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+ # Create the measurement file if needed
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+ if [ ! -f "$MEASUREMENT_FILE" ]; then
139
+ echo "Creating $MEASUREMENT_FILE"
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+ # Create directories
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+ if [ -d "$OUTPUT_DIR" ]; then
142
+ rm -r "$OUTPUT_DIR"
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+ fi
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+ mkdir "$OUTPUT_DIR"
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+
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+ python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
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+ fi
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+
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+ # Choose one of the below. Either create a single quant for testing or a batch of them.
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+ # BIT_PRECISIONS=(5.0)
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+ BIT_PRECISIONS=(8.0 7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25)
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+
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+ for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
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+ do
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+ CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
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+
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+ # If it doesn't already exist, make the quant
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+ if [ ! -d "$CONVERTED_FOLDER" ]; then
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+
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+ echo "Creating $CONVERTED_FOLDER"
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+
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+ # Create directories
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+ if [ -d "$OUTPUT_DIR" ]; then
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+ rm -r "$OUTPUT_DIR"
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+ fi
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+ mkdir "$OUTPUT_DIR"
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+ mkdir "$CONVERTED_FOLDER"
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+
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+ # Run conversion commands
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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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+
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+ fi
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+ done
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+ ```
config.json ADDED
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+ {
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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": "4.36.0",
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+ "use_cache": true,
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+ "vocab_size": 32000,
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+ "quantization_config": {
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+ "quant_method": "exl2",
30
+ "version": "0.0.18",
31
+ "bits": 4.5,
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+ "head_bits": 6,
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+ "calibration": {
34
+ "rows": 100,
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+ "length": 2048,
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+ "dataset": "(default)"
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+ }
38
+ }
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.36.0"
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+ }
huggingface-metadata.txt ADDED
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+ url: https://huggingface.co/152334H/miqu-1-70b-sf
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+ branch: main
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+ download date: 2024-02-02 21:23:11
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+ sha256sum:
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