Added GPU VRAM info for different models in constants.py

Added information about GPU VRAM Memory required for LLM Models by Billion Parameter value (B Model). This adds information for different quantization levels as well.

constants.py

@@ -44,6 +44,14 @@
 # Select the Model ID and model_basename
 # load the LLM for generating Natural Language responses
 
+#### GPU VRAM Memory required for LLM Models by Billion Parameter value (B Model)
+####
+#### (B Model) (float32) (float16) (GPTQ 8bit) (GPTQ 4bit)
+#### 7b 28 GB 14 GB 7 GB 3.5 GB 
+#### 13b 52 GB 26 GB 13 GB 6.5 GB 
+#### 32b 130 GB 65 GB 32.5 GB 16.25 GB 
+#### 65b 260.8 GB 130.4 GB 65.2 GB 32.6 GB 
+
 MODEL_ID = "TheBloke/Llama-2-7B-Chat-GGML"
 MODEL_BASENAME = "llama-2-7b-chat.ggmlv3.q4_0.bin"
 
@@ -56,16 +64,47 @@
 # alongside will 100% create OOM on 24GB cards.
 # llm = load_model(device_type, model_id=model_id)
 
-# for GPTQ (quantized) models
-# MODEL_ID = "TheBloke/Nous-Hermes-13B-GPTQ"
-# MODEL_BASENAME = "nous-hermes-13b-GPTQ-4bit-128g.no-act.order"
-# MODEL_ID = "TheBloke/WizardLM-30B-Uncensored-GPTQ"
-# MODEL_BASENAME = "WizardLM-30B-Uncensored-GPTQ-4bit.act-order.safetensors" # Requires
-# ~21GB VRAM. Using STransformers alongside can potentially create OOM on 24GB cards.
-# MODEL_ID = "TheBloke/wizardLM-7B-GPTQ"
-# MODEL_BASENAME = "wizardLM-7B-GPTQ-4bit.compat.no-act-order.safetensors"
-# MODEL_ID = "TheBloke/WizardLM-7B-uncensored-GPTQ"
-# MODEL_BASENAME = "WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
+ # For GPTQ (quantized) select an llm model based on your GPU and VRAM GB
+
+ ##### 48GB VRAM Graphics Cards (RTX 6000, RTX A6000 and other 48GB VRAM GPUs) #####
+
+ ### 65b GPTQ Models for 48GB GPUs
+ # model_id = "TheBloke/guanaco-65B-GPTQ"
+ # model_basename = "model.safetensors"
+ # model_id = "TheBloke/Airoboros-65B-GPT4-2.0-GPTQ"
+ # model_basename = "model.safetensors"
+ # model_id = "TheBloke/gpt4-alpaca-lora_mlp-65B-GPTQ"
+ # model_basename = "model.safetensors"
+ # model_id = "TheBloke/Upstage-Llama1-65B-Instruct-GPTQ" 
+ # model_basename = "model.safetensors" 
+
+ ##### 24GB VRAM Graphics Cards (RTX 3090 - RTX 4090 (35% Faster) - RTX A5000 - RTX A5500) #####
+
+ ### 13b GPTQ Models for 24GB GPUs (*** With best embedding model: hkunlp/instructor-xl ***)
+ # model_id = "TheBloke/Wizard-Vicuna-13B-Uncensored-GPTQ"
+ # model_basename = "Wizard-Vicuna-13B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
+ # model_id = "TheBloke/vicuna-13B-v1.5-GPTQ"
+ # model_basename = "model.safetensors"
+ # model_id = "TheBloke/Nous-Hermes-13B-GPTQ"
+ # model_basename = "nous-hermes-13b-GPTQ-4bit-128g.no-act.order"
+ # model_id = "TheBloke/WizardLM-13B-V1.2-GPTQ" 
+ # model_basename = "gptq_model-4bit-128g.safetensors
+
+ ### 30b GPTQ Models for 24GB GPUs (*** Requires using intfloat/e5-base-v2 instead of hkunlp/instructor-large as embedding model ***)
+ # model_id = "TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ"
+ # model_basename = "Wizard-Vicuna-30B-Uncensored-GPTQ-4bit--1g.act.order.safetensors" 
+ # model_id = "TheBloke/WizardLM-30B-Uncensored-GPTQ"
+ # model_basename = "WizardLM-30B-Uncensored-GPTQ-4bit.act-order.safetensors" 
+
+ ##### 8-10GB VRAM Graphics Cards (RTX 3080 - RTX 3080 Ti - RTX 3070 Ti - 3060 Ti - RTX 2000 Series, Quadro RTX 4000, 5000, 6000) #####
+
+ ### 7b GPTQ Models for 8GB GPUs
+ # model_id = "TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ"
+ # model_basename = "Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.no-act.order.safetensors"
+ # model_id = "TheBloke/WizardLM-7B-uncensored-GPTQ"
+ # model_basename = "WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
+ # model_id = "TheBloke/wizardLM-7B-GPTQ"
+ # model_basename = "wizardLM-7B-GPTQ-4bit.compat.no-act-order.safetensors"
 
 # for GGML (quantized cpu+gpu+mps) models - check if they support llama.cpp
 # MODEL_ID = "TheBloke/wizard-vicuna-13B-GGML"
@@ -74,5 +113,3 @@
 # MODEL_BASENAME = "wizard-vicuna-13B.ggmlv3.q2_K.bin"
 # MODEL_ID = "TheBloke/orca_mini_3B-GGML"
 # MODEL_BASENAME = "orca-mini-3b.ggmlv3.q4_0.bin"
-
-