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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ja
+- ko
+- zh
+- ar
+license: cc-by-nc-4.0
+tags:
+- exl2
+---
+
+# c4ai-command-r-v01 - EXL2 3.5bpw
+
+This is a 3.5bpw EXL2 quant of [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
+
+Details about the model can be found at the above model page.
+
+
+## EXL2 Version
+
+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.
+
+If you have problems loading these models, please update Text Generation WebUI to the latest version.
+
+### RP Calibrated
+
+The rpcal quants were made using data/PIPPA-cleaned/pippa_raw_fix.parquet for calibration.
+
+## Perplexity Scoring
+
+Below are the perplexity scores for the EXL2 models. A lower score is better. 
+ 
+### Stock Quants
+
+| Quant Level | Perplexity Score |
+|-------------|------------------|
+| 8.0 | 6.4436 |
+| 7.0 | 6.4372 |
+| 6.0 | 6.4391 |
+| 5.0 | 6.4526 |
+| 4.5 | 6.4629 |
+| 4.0 | 6.5081 |
+| 3.5 | 6.6301 |
+| 3.0 | 6.7974 |
+
+### RP Calibrated Quants
+
+| Quant Level | Perplexity Score |
+|-------------|------------------|
+| 8.0 | 6.4331 |
+| 7.0 | 6.4347 |
+| 6.0 | 6.4356 |
+| 5.0 | 6.4740 |
+| 4.5 | 6.4875 |
+| 4.0 | 6.5039 |
+| 3.5 | 6.6928 |
+| 3.0 | 6.8913 |
+
+## EQ Bench
+
+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.
+
+### Quants
+
+| Quant Size | Instruct Template | Score |
+|------------|-------------------|-------|
+| 8.0 | Alpaca | 56.67 |
+| 8.0 | ChatML | 47.28 |
+| 8.0 | Command-R | 58.46 |
+| 8.0 | Command-R-Plus | 58.49 |
+| 7.0 | Alpaca | 57.5 |
+| 7.0 | ChatML | 46.86 |
+| 7.0 | Command-R | 57.29 |
+| 7.0 | Command-R-Plus | 57.91 |
+| 6.0 | Alpaca | 56.5 |
+| 6.0 | ChatML | 48.61 |
+| 6.0 | Command-R | 57.8 |
+| 6.0 | Command-R-Plus | 58.64 |
+| 5.0 | Alpaca | 54.64 |
+| 5.0 | ChatML | 48.48 |
+| 5.0 | Command-R | 57.14 |
+| 5.0 | Command-R-Plus | 56.63 |
+| 4.5 | Alpaca | 57.75 |
+| 4.5 | ChatML | 48.1 |
+| 4.5 | Command-R | 57.08 |
+| 4.5 | Command-R-Plus | 56.7 |
+| 4.0 | Alpaca | 53.41 |
+| 4.0 | ChatML | 50.99 |
+| 4.0 | Command-R | 57.46 |
+| 4.0 | Command-R-Plus | 57.99 |
+| 3.5 | Alpaca | 56.68 |
+| 3.5 | ChatML | 52.72 |
+| 3.5 | Command-R | 60.91 |
+| 3.5 | Command-R-Plus | 60.91 |
+| 3.0 | Alpaca | 36.45 |
+| 3.0 | ChatML | 39.19 |
+| 3.0 | Command-R | 49.17 |
+| 3.0 | Command-R-Plus | 49.68 |
+
+
+### RP Calibrated Quants
+
+| Quant Size | Instruct Template | Score |
+|------------|-------------------|-------|
+| 8.0 | Alpaca | 56.23 |
+| 8.0 | ChatML | 48.42 |
+| 8.0 | Command-R | 58.41 |
+| 8.0 | Command-R-Plus | 58.41 |
+| 7.0 | Alpaca | 57.01 |
+| 7.0 | ChatML | 48.47 |
+| 7.0 | Command-R | 57.85 |
+| 7.0 | Command-R-Plus | 57.67 |
+| 6.0 | Alpaca | 58.33 |
+| 6.0 | ChatML | 50.93 |
+| 6.0 | Command-R | 60.32 |
+| 6.0 | Command-R-Plus | 59.83 |
+| 5.0 | Alpaca | 55.28 |
+| 5.0 | ChatML | 50.29 |
+| 5.0 | Command-R | 58.96 |
+| 5.0 | Command-R-Plus | 59.23 |
+| 4.5 | Alpaca | 55.01 |
+| 4.5 | ChatML | 46.63 |
+| 4.5 | Command-R | 57.7 |
+| 4.5 | Command-R-Plus | 59.24 |
+| 4.0 | Alpaca | 49.76 |
+| 4.0 | ChatML | 47.13 |
+| 4.0 | Command-R | 54.76 |
+| 4.0 | Command-R-Plus | 55.5 |
+| 3.5 | Alpaca | 56.39 |
+| 3.5 | ChatML | 52.98 |
+| 3.5 | Command-R | 59.19 |
+| 3.5 | Command-R-Plus | 58.32 |
+| 3.0 | Alpaca | 50.36 |
+| 3.0 | ChatML | 47.94 |
+| 3.0 | Command-R | 54.89 |
+| 3.0 | Command-R-Plus | 53.61 |
+
+
+### Command-R-Plus Template
+
+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.
+
+_text-generation-webui/instruction-templates/Command-R-Plus.yaml_:
+```yaml
+instruction_template: |-
+  {%- if messages[0]['role'] == 'system' -%}
+      {%- set loop_messages = messages[1:] -%}
+      {%- set system_message = messages[0]['content'] -%}
+  {%- elif false == true -%}
+      {%- set loop_messages = messages -%}
+      {%- 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.' -%}
+  {%- else -%}
+      {%- set loop_messages = messages -%}
+      {%- set system_message = false -%}
+  {%- endif -%}
+  {%- if system_message != false -%}
+      {{ '<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}
+  {%- endif -%}
+  {%- for message in loop_messages -%}
+      {%- set content = message['content'] -%}
+      {%- if message['role'] == 'user' -%}
+          {{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}
+      {%- elif message['role'] == 'assistant' -%}
+          {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- if add_generation_prompt -%}
+      {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
+  {%- endif -%}
+
+```
+
+### Perplexity Script
+
+This was the script used for perplexity testing.
+
+```bash
+#!/bin/bash
+
+# Activate the conda environment
+source ~/miniconda3/etc/profile.d/conda.sh
+conda activate exllamav2
+
+# Set the model name and bit size
+MODEL_NAME="c4ai-command-r-v01"
+BIT_PRECISIONS=(8.0 7.0 6.0 5.0 4.5 4.0 3.5 3.0)
+
+# Print the markdown table header
+echo "| Quant Level | Perplexity Score |"
+echo "|-------------|------------------|"
+
+for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
+do
+  MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
+  # MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw-rpcal"
+  if [ -d "$MODEL_DIR" ]; then
+    output=$(python test_inference.py -m "$MODEL_DIR" -gs 22,24 -ed data/wikitext/wikitext-2-v1.parquet)
+    score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+')
+    echo "| $BIT_PRECISION | $score |"
+  fi
+done
+```
+
+
+## Quant Details
+
+This is the script used for quantization.
+
+```bash
+#!/bin/bash
+
+# Activate the conda environment
+source ~/miniconda3/etc/profile.d/conda.sh
+conda activate exllamav2
+
+# Set the model name and bit size
+MODEL_NAME="c4ai-command-r-v01"
+
+# Define variables
+MODEL_DIR="models/$MODEL_NAME"
+OUTPUT_DIR="exl2_$MODEL_NAME"
+MEASUREMENT_FILE="measurements/$MODEL_NAME.json"
+# CALIBRATION_DATASET="data/PIPPA-cleaned/pippa_raw_fix.parquet"
+
+# Create the measurement file if needed
+if [ ! -f "$MEASUREMENT_FILE" ]; then
+    echo "Creating $MEASUREMENT_FILE"
+    # Create directories
+    if [ -d "$OUTPUT_DIR" ]; then
+        rm -r "$OUTPUT_DIR"
+    fi
+    mkdir "$OUTPUT_DIR"
+
+    # python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE -c $CALIBRATION_DATASET
+    python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
+fi
+
+# Choose one of the below. Either create a single quant for testing or a batch of them.
+# BIT_PRECISIONS=(5.0)
+BIT_PRECISIONS=(8.0 7.0 6.0 5.0 4.5 4.0 3.5 3.0)
+
+for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
+do
+    CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
+
+    # If it doesn't already exist, make the quant
+    if [ ! -d "$CONVERTED_FOLDER" ]; then
+
+        echo "Creating $CONVERTED_FOLDER"
+
+        # Create directories
+        if [ -d "$OUTPUT_DIR" ]; then
+            rm -r "$OUTPUT_DIR"
+        fi
+        mkdir "$OUTPUT_DIR"
+        mkdir "$CONVERTED_FOLDER"
+        
+        # Run conversion commands  
+        # python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -c $CALIBRATION_DATASET -cf $CONVERTED_FOLDER
+        python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER
+
+    fi
+done
+```

config.json

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+{
+    "_name_or_path": "/home/ahmet_cohere_com/HF_Final_weight_tie",
+    "architectures": [
+        "CohereForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 5,
+    "eos_token_id": 255001,
+    "hidden_act": "silu",
+    "hidden_size": 8192,
+    "initializer_range": 0.02,
+    "intermediate_size": 22528,
+    "layer_norm_eps": 1e-05,
+    "logit_scale": 0.0625,
+    "max_position_embeddings": 8192,
+    "model_max_length": 131072,
+    "model_type": "cohere",
+    "num_attention_heads": 64,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 64,
+    "pad_token_id": 0,
+    "pretraining_tp": 1,
+    "rope_theta": 8000000.0,
+    "torch_dtype": "float16",
+    "transformers_version": "4.38.2",
+    "use_cache": true,
+    "vocab_size": 256000,
+    "tie_word_embeddings": true,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.18",
+        "bits": 3.5,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "pippa_raw_fix.parquet"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 5,
+  "eos_token_id": 255001,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.2"
+}

model.safetensors.index.json

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