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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* 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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  *.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
@@ -0,0 +1,271 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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-v01 - EXL2 4.5bpw
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+
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+ This is a 4.5bpw EXL2 quant of [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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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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+ ### RP Calibrated
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+
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+ The rpcal quants were made using data/PIPPA-cleaned/pippa_raw_fix.parquet for calibration.
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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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+ ### Stock Quants
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+
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+ | Quant Level | Perplexity Score |
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+ |-------------|------------------|
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+ | 8.0 | 6.4436 |
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+ | 7.0 | 6.4372 |
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+ | 6.0 | 6.4391 |
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+ | 5.0 | 6.4526 |
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+ | 4.5 | 6.4629 |
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+ | 4.0 | 6.5081 |
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+ | 3.5 | 6.6301 |
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+ | 3.0 | 6.7974 |
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+
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+ ### RP Calibrated Quants
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+
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+ | Quant Level | Perplexity Score |
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+ |-------------|------------------|
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+ | 8.0 | 6.4331 |
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+ | 7.0 | 6.4347 |
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+ | 6.0 | 6.4356 |
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+ | 5.0 | 6.4740 |
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+ | 4.5 | 6.4875 |
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+ | 4.0 | 6.5039 |
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+ | 3.5 | 6.6928 |
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+ | 3.0 | 6.8913 |
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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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+ ### Quants
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+
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+ | Quant Size | Instruct Template | Score |
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+ |------------|-------------------|-------|
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+ | 8.0 | Alpaca | 56.67 |
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+ | 8.0 | ChatML | 47.28 |
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+ | 8.0 | Command-R | 58.46 |
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+ | 8.0 | Command-R-Plus | 58.49 |
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+ | 7.0 | Alpaca | 57.5 |
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+ | 7.0 | ChatML | 46.86 |
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+ | 7.0 | Command-R | 57.29 |
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+ | 7.0 | Command-R-Plus | 57.91 |
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+ | 6.0 | Alpaca | 56.5 |
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+ | 6.0 | ChatML | 48.61 |
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+ | 6.0 | Command-R | 57.8 |
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+ | 6.0 | Command-R-Plus | 58.64 |
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+ | 5.0 | Alpaca | 54.64 |
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+ | 5.0 | ChatML | 48.48 |
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+ | 5.0 | Command-R | 57.14 |
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+ | 5.0 | Command-R-Plus | 56.63 |
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+ | 4.5 | Alpaca | 57.75 |
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+ | 4.5 | ChatML | 48.1 |
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+ | 4.5 | Command-R | 57.08 |
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+ | 4.5 | Command-R-Plus | 56.7 |
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+ | 4.0 | Alpaca | 53.41 |
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+ | 4.0 | ChatML | 50.99 |
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+ | 4.0 | Command-R | 57.46 |
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+ | 4.0 | Command-R-Plus | 57.99 |
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+ | 3.5 | Alpaca | 56.68 |
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+ | 3.5 | ChatML | 52.72 |
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+ | 3.5 | Command-R | 60.91 |
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+ | 3.5 | Command-R-Plus | 60.91 |
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+ | 3.0 | Alpaca | 36.45 |
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+ | 3.0 | ChatML | 39.19 |
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+ | 3.0 | Command-R | 49.17 |
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+ | 3.0 | Command-R-Plus | 49.68 |
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+
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+
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+ ### RP Calibrated Quants
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+
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+ | Quant Size | Instruct Template | Score |
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+ |------------|-------------------|-------|
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+ | 8.0 | Alpaca | 56.23 |
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+ | 8.0 | ChatML | 48.42 |
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+ | 8.0 | Command-R | 58.41 |
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+ | 8.0 | Command-R-Plus | 58.41 |
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+ | 7.0 | Alpaca | 57.01 |
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+ | 7.0 | ChatML | 48.47 |
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+ | 7.0 | Command-R | 57.85 |
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+ | 7.0 | Command-R-Plus | 57.67 |
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+ | 6.0 | Alpaca | 58.33 |
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+ | 6.0 | ChatML | 50.93 |
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+ | 6.0 | Command-R | 60.32 |
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+ | 6.0 | Command-R-Plus | 59.83 |
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+ | 5.0 | Alpaca | 55.28 |
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+ | 5.0 | ChatML | 50.29 |
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+ | 5.0 | Command-R | 58.96 |
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+ | 5.0 | Command-R-Plus | 59.23 |
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+ | 4.5 | Alpaca | 55.01 |
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+ | 4.5 | ChatML | 46.63 |
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+ | 4.5 | Command-R | 57.7 |
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+ | 4.5 | Command-R-Plus | 59.24 |
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+ | 4.0 | Alpaca | 49.76 |
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+ | 4.0 | ChatML | 47.13 |
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+ | 4.0 | Command-R | 54.76 |
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+ | 4.0 | Command-R-Plus | 55.5 |
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+ | 3.5 | Alpaca | 56.39 |
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+ | 3.5 | ChatML | 52.98 |
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+ | 3.5 | Command-R | 59.19 |
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+ | 3.5 | Command-R-Plus | 58.32 |
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+ | 3.0 | Alpaca | 50.36 |
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+ | 3.0 | ChatML | 47.94 |
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+ | 3.0 | Command-R | 54.89 |
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+ | 3.0 | Command-R-Plus | 53.61 |
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+
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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-v01"
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+ BIT_PRECISIONS=(8.0 7.0 6.0 5.0 4.5 4.0 3.5 3.0)
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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_${BIT_PRECISION}bpw-rpcal"
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+ if [ -d "$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 | $score |"
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+ fi
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+ done
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+ ```
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+
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+
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+ ## Quant Details
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+
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+ This is the script used for quantization.
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+
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+ ```bash
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+ #!/bin/bash
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+
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+ # Activate the conda environment
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+ source ~/miniconda3/etc/profile.d/conda.sh
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+ conda activate exllamav2
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+
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+ # Set the model name and bit size
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+ MODEL_NAME="c4ai-command-r-v01"
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+
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+ # Define variables
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+ MODEL_DIR="models/$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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+ # CALIBRATION_DATASET="data/PIPPA-cleaned/pippa_raw_fix.parquet"
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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 -c $CALIBRATION_DATASET
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+ python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
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+ fi
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+
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+ # Choose one of the below. Either create a single quant for testing or a batch of them.
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+ # BIT_PRECISIONS=(5.0)
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+ BIT_PRECISIONS=(8.0 7.0 6.0 5.0 4.5 4.0 3.5 3.0)
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+
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+ for BIT_PRECISION in "${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 -c $CALIBRATION_DATASET -cf $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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+
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+ fi
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+ done
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+ ```
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+ {
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+ "_name_or_path": "/home/ahmet_cohere_com/HF_Final_weight_tie",
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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": 8192,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 22528,
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+ "logit_scale": 0.0625,
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+ "model_type": "cohere",
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+ "rope_theta": 8000000.0,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.38.2",
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+ "use_cache": true,
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+ "vocab_size": 256000,
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+ "tie_word_embeddings": true,
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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": 4.5,
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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": "pippa_raw_fix.parquet"
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+ }
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+ }
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "transformers_version": "4.38.2"
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+ }
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