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.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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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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+ - fr
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+ - de
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+ - es
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+ - it
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+ - pt
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+ - ja
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+ - ko
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+ - zh
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+ - ar
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+ license: cc-by-nc-4.0
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+ tags:
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+ - exl2
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+ ---
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+
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+ # c4ai-command-r-plus - EXL2 7.0bpw
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+
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+ This is a 7.0bpw EXL2 quant of [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
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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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+ ## Turbodep EXL2 Quants
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+
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+ This repo only has specific quants not already done at [turboderp/command-r-plus-103B-exl2](https://huggingface.co/turboderp/command-r-plus-103B-exl2)
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+
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+ Quants marked as turboderp can be downloaded from that repo.
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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 | Repo |
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+ |-------------|------------------|------|
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+ | 6.0 | 4.7068 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 5.0 | 4.7309 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 4.5 | 4.8111 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 4.25 | 4.8292 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 4.0 | 4.8603 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 3.75 | 4.9112 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 3.5 | 4.9592 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 3.25 | 5.0631 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 3.0 | 5.2050 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+ | 2.5 | 5.6681 | [turboderp](https://huggingface.co/turboderp/command-r-plus-103B-exl2) |
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+
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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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+ _TODO_
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+
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+ ### Command-R-Plus Template
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+
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+ This is the Command-R-Plus template yaml that was used in EQ bench(which uses Text Generation Web UI yaml templates). It adds BOS_TOKEN into the starter prompt.
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+
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+ _text-generation-webui/instruction-templates/Command-R-Plus.yaml_:
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+ ```yaml
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+ instruction_template: |-
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+ {%- if messages[0]['role'] == 'system' -%}
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+ {%- set loop_messages = messages[1:] -%}
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+ {%- set system_message = messages[0]['content'] -%}
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+ {%- elif false == true -%}
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+ {%- set loop_messages = messages -%}
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+ {%- set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' -%}
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+ {%- else -%}
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+ {%- set loop_messages = messages -%}
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+ {%- set system_message = false -%}
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+ {%- endif -%}
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+ {%- if system_message != false -%}
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+ {{ '<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}
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+ {%- endif -%}
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+ {%- for message in loop_messages -%}
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+ {%- set content = message['content'] -%}
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+ {%- if message['role'] == 'user' -%}
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+ {{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}
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+ {%- elif message['role'] == 'assistant' -%}
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+ {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- if add_generation_prompt -%}
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+ {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
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+ {%- endif -%}
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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="c4ai-command-r-plus"
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+ BIT_PRECISIONS=(8.0 7.5 7.0 6.5 5.5 2.75 2.25)
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+
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+ # MODEL_NAME="turboderp_command-r-plus-103B"
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+ # BIT_PRECISIONS=(6.0 5.0 4.5 4.25 4.0 3.75 3.5 3.25 3.0 2.5)
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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_${BIT_PRECISION}bpw"
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+ # MODEL_DIR="models/${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 | $score |"
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+ fi
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+ done
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+ ```
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+
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+
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+ ## Quant Details
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+
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+ This is the script used for quantization.
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+
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+ ```bash
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+ #!/bin/bash
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+
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+ # Activate the conda environment
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+ source ~/miniconda3/etc/profile.d/conda.sh
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+ conda activate exllamav2
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+
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+ # Set the model name and bit size
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+ MODEL_NAME="c4ai-command-r-plus"
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+
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+ # Define variables
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+ MODEL_DIR="models/$MODEL_NAME"
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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
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+ echo "Creating $MEASUREMENT_FILE"
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+ # Create directories
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+ if [ -d "$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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+
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+ python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
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+ fi
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+
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+ # Choose one of the below. Either create a single quant for testing or a batch of them.
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+ # BIT_PRECISIONS=(5.0)
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+ BIT_PRECISIONS=(8.0 7.5 6.5 5.5 2.75 2.25)
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+
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+ for BIT_PRECISION in "${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
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "architectures": [
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+ "CohereForCausalLM"
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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": 5,
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+ "eos_token_id": 255001,
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+ "hidden_act": "silu",
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+ "hidden_size": 12288,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 33792,
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+ "layer_norm_eps": 1e-05,
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+ "logit_scale": 0.8333333333333334,
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+ "max_position_embeddings": 8192,
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+ "model_max_length": 131072,
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+ "model_type": "cohere",
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+ "num_attention_heads": 96,
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+ "num_hidden_layers": 64,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 0,
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+ "rope_theta": 75000000.0,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.40.0.dev0",
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+ "use_cache": true,
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+ "use_qk_norm": true,
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+ "vocab_size": 256000,
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+ "quantization_config": {
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+ "quant_method": "exl2",
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+ "version": "0.0.18",
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+ "bits": 7.0,
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+ "head_bits": 6,
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+ "calibration": {
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+ "rows": 100,
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+ "length": 2048,
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+ "dataset": "(default)"
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+ }
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+ }
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+ }
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 5,
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+ "eos_token_id": 255001,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.40.0.dev0"
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+ }
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