First commit

.gitignore

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+__pycache__
+.cog
+cache/
+Llama-2-7b-Chat-GPTQ/

README.md

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+# TheBloke/Llama-2-7b-Chat-GPTQ Cog model
+
+This is an implementation of the [TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/SG161222/Realistic_Vision_V3.0_VAE) as a Cog model. [Cog packages machine learning models as standard containers.](https://github.com/replicate/cog)
+
+First, download the pre-trained weights:
+
+    cog run script/download-weights
+
+Then, you can run predictions:
+
+    cog predict -i prompt="Tell me about AI"

cog.yaml

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+build:
+  # set to true if your model requires a GPU
+  gpu: true
+  cuda: "11.8"
+  python_version: "3.10"
+  python_packages:
+    - "torch==2.0.0"
+    # - "transformers==4.31.0"
+    - "accelerate==0.21.0"
+    - "safetensors==0.3.1"
+    - "auto_gptq"
+    - "peft"
+    - "numpy"
+    - "rouge"
+    - "datasets"
+
+  run:
+    - pip install git+https://github.com/huggingface/transformers
+    - pip install xformers
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

predict.py

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+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+from cog import BasePredictor, Input
+import torch
+from transformers import AutoTokenizer, pipeline
+from auto_gptq import AutoGPTQForCausalLM
+
+MODEL_NAME = "TheBloke/Llama-2-7b-Chat-GPTQ"
+MODEL_BASENAME = "gptq_model-4bit-128g"
+MODEL_CACHE = "cache"
+use_triton = True
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            MODEL_NAME, 
+            use_fast=True,
+            cache_dir=MODEL_CACHE
+        )
+        model = AutoGPTQForCausalLM.from_quantized(
+            "Llama-2-7b-Chat-GPTQ",
+            use_safetensors=True,
+            device="cuda:0",
+            use_triton=use_triton,
+            quantize_config=None
+        )
+        # Pytorch 2 optimization
+        self.model = torch.compile(model)
+
+    def predict(
+        self,
+        prompt: str = Input(description="Prompt to send to Llama v2", default="Tell me about AI"),
+        system_prompt: str = Input(description="System prompt that helps guide system behavior", default="You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."),
+        max_new_tokens: int = Input(description="Number of new tokens", ge=1, le=4096 , default=512),
+        temperature: float = Input(description="Randomness of outputs, 0 is deterministic, greater than 1 is random", ge=0, le=5, default=0.75),
+        # top_p: float = Input(description="When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens", ge=0.01, le=1, default=0.95),
+        # repetition_penalty: float = Input(description="Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it", ge=0, le=5, default=1.1),
+    ) -> str:
+        """Run a single prediction on the model"""
+        prompt_template=f'''[INST] <<SYS>>
+        {system_prompt}
+        <</SYS>>
+        {prompt}[/INST]'''
+
+        input_ids = self.tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+        outputs = self.model.generate(inputs=input_ids, temperature=temperature, max_new_tokens=max_new_tokens)
+        output = self.tokenizer.decode(outputs[0])
+        parts = output.split("[/INST]", 1)
+        final = parts[1]
+
+        return final
+

script/download-weights

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+#!/usr/bin/env python
+
+import os
+import sys
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM
+
+# append project directory to path so predict.py can be imported
+sys.path.append('.')
+
+from predict import MODEL_NAME, MODEL_BASENAME, MODEL_CACHE
+use_triton = True
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_NAME,
+    use_fast=True,
+    cache_dir=MODEL_CACHE,
+)
+
+# model = AutoGPTQForCausalLM.from_quantized(
+#     MODEL_NAME,
+#     model_basename=MODEL_BASENAME,
+#     use_safetensors=True,
+#     trust_remote_code=True,
+#     device="cuda:0",
+#     use_triton=use_triton,
+#     quantize_config=None,
+# )
+# model.save_quantized("cache-gptq", use_safetensors=True)
+os.system("git clone --branch main https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ")