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--- a/.gitattributes
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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--- a/1/model.py
+++ b/1/model.py
@ -1,255 +0,0 @@
-"""
-[Transformer-LLM 백엔드 가이드]
-
-본 파일은 NVIDIA Triton Server에서 Hugging Face `AutoModelForCausalLM` 기반 모델을 손쉽게 배포하기 위해 제공되는 커스텀 Python 백엔드 템플릿입니다.
-
-1. 모델 호환성
-   - Hugging Face의 `AutoModelForCausalLM` 클래스와 호환되는 모든 Causal Language Model을 지원합니다.
-   - [확인] 배포할 모델 `config.json`의 `architectures` 항목이 `...ForCausalLM` 형식인지 확인.
-   - [확인] 모델이 Hugging Face 공식 문서의 AutoModelForCausalLM이 지원하는 모델인지 확인.
-           (https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoModelForCausalLM.from_pretrained)
-
-2. 토크나이저 호환성
-   - `AutoTokenizer`와 호환되는 토크나이저를 지원하며, 모델과 동일한 경로에서 자동으로 로드됩니다.
-
-3. 커스터마이징 안내
-   - 본 템플릿은 범용적인 사용을 위해 작성되었습니다.
-   - 특정 모델의 동작 방식이나 예외 처리가 필요한 경우, 이 파일(`model.py`)과 설정 파일(`config.pbtxt`)을 직접 수정하여 사용하시기 바랍니다.
-"""
-
-import json
-import torch
-import numpy as np
-import triton_python_backend_utils as pb_utils
-import uuid
-from typing import List, Dict, Any, Union, Tuple
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    GenerationConfig,
-    BitsAndBytesConfig,
-)
-from peft import PeftModel, PeftConfig
-
-class TritonPythonModel:
-    def initialize(self, args: Dict[str, str]):
-        """
-        모델 초기화: 설정 로드, 로거 설정, 모델 및 토크나이저 로드
-        """
-        self.logger = pb_utils.Logger
-        self.model_config = json.loads(args["model_config"])
-        self.model_name = args["model_name"]
-
-        # 설정 파라미터 로드
-        self.base_model_path = self._get_config_param("base_model_path")
-        self.is_adapter_model = self._get_config_param("is_adapter_model", "false").lower() == "true"
-        self.adapter_model_path = self._get_config_param("adapter_model_path")
-        self.quantization = self._get_config_param("quantization")
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        # 설정 로그 출력
-        self.logger.log_info(f"================ {self.model_name} Setup ================")
-        self.logger.log_info(f"Base Model: {self.base_model_path}")
-        self.logger.log_info(f"Adapter Mode: {self.is_adapter_model} ({self.adapter_model_path})")
-        self.logger.log_info(f"Quantization: {self.quantization}")
-        self.logger.log_info(f"Device: {self.device}")
-
-        self._load_model_and_tokenizer()
-        self.logger.log_info(f"Model initialized successfully.")
-
-    def _load_model_and_tokenizer(self):
-        """모델과 토크나이저를 로드하고 설정합니다."""
-        # 1. Quantization 설정
-        bnb_config = self._get_bnb_config()
-
-        # 2. Base Model 로드
-        load_path = self.base_model_path
-        if self.is_adapter_model:
-            peft_config = PeftConfig.from_pretrained(self.adapter_model_path)
-            load_path = peft_config.base_model_name_or_path
-
-        try:
-            self.model = AutoModelForCausalLM.from_pretrained(
-                load_path,
-                torch_dtype="auto",
-                quantization_config=bnb_config,
-                device_map="auto",
-                local_files_only=True,
-                trust_remote_code=True
-            )
-        except Exception as e:
-            self.logger.log_error(f"Failed to load base model: {e}")
-            raise e
-
-        # 3. Adapter 병합 (필요 시)
-        if self.is_adapter_model:
-            self.model = PeftModel.from_pretrained(self.model, self.adapter_model_path)
-
-        self.model.eval()
-
-        # 4. Tokenizer 로드
-        self.tokenizer = AutoTokenizer.from_pretrained(load_path, trust_remote_code=True)
-
-        if self.tokenizer.pad_token is None:
-            self.tokenizer.pad_token = self.tokenizer.eos_token
-            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-            self.logger.log_info("Pad token was None. Set to EOS token.")
-
-        self.supports_chat_template = (
-            hasattr(self.tokenizer, "chat_template") and
-            self.tokenizer.chat_template is not None
-        )
-
-        self.logger.log_info(f"Supports Chat Template: {self.supports_chat_template}")
-        if self.supports_chat_template:
-            self.logger.log_info(f"Chat Template Content:\n{self.tokenizer.chat_template}")
-
-    def _get_bnb_config(self) -> Union[BitsAndBytesConfig, None]:
-        if self.quantization == "int4":
-            return BitsAndBytesConfig(
-                load_in_4bit=True,
-                bnb_4bit_use_double_quant=True,
-                bnb_4bit_quant_type="nf4",
-                bnb_4bit_compute_dtype=torch.float16
-            )
-        elif self.quantization == "int8":
-            return BitsAndBytesConfig(
-                load_in_8bit=True,
-                llm_int8_threshold=6.0,
-                llm_int8_has_fp16_weight=True
-            )
-        return None
-
-    def execute(self, requests):
-        """Triton Inference Request 처리 메인 루프"""
-        responses = []
-
-        for request in requests:
-            # [ID 생성 로직] - 로그 추적용으로 유지 (Response에는 포함 X)
-            request_id = request.request_id()
-            if not request_id:
-                request_id = str(uuid.uuid4())
-
-            try:
-                # 1. 입력 데이터 파싱
-                input_data, is_chat = self._parse_input(request)
-
-                # [LOGGING] Request ID 포함하여 로그 출력
-                log_input_str = json.dumps(input_data, ensure_ascii=False) if isinstance(input_data, (list, dict)) else str(input_data)
-                self.logger.log_info(f"\n[RID: {request_id}] >>> [{'CHAT' if is_chat else 'TEXT'}][Input]: {log_input_str}")
-
-                # 2. Generation Config 생성
-                gen_config = self._create_generation_config(request)
-
-                # 3. 토크나이징
-                inputs = self._tokenize(input_data, is_chat)
-
-                # 4. 모델 추론 (Generate)
-                output_text = self._generate(inputs, gen_config)
-
-                # [LOGGING] Request ID 포함하여 결과 출력
-                self.logger.log_info(f"\n[RID: {request_id}] <<< [Output]: {output_text}")
-
-                # 5. 응답 생성
-                responses.append(self._create_response(output_text, request_id))
-
-            except Exception as e:
-                self.logger.log_error(f"[RID: {request_id}] Error during execution: {e}")
-                err_tensor = pb_utils.Tensor("text_output", np.array([str(e).encode('utf-8')], dtype=np.bytes_))
-                responses.append(pb_utils.InferenceResponse(output_tensors=[err_tensor]))
-
-        return responses
-
-    def _parse_input(self, request) -> Tuple[Union[str, List[Dict]], bool]:
-        input_text = self._get_input_scalar(request, "text_input")
-        try:
-            conversation = json.loads(input_text)
-            if isinstance(conversation, list):
-                return conversation, True
-        except (json.JSONDecodeError, TypeError):
-            pass
-        return input_text, False
-
-    def _tokenize(self, input_data, is_chat: bool):
-        if self.supports_chat_template and is_chat:
-            return self.tokenizer.apply_chat_template(
-                input_data,
-                tokenize=True,
-                add_generation_prompt=True,
-                return_tensors="pt",
-                return_dict=True
-            ).to(self.device)
-        else:
-            if is_chat:
-                 input_data = str(input_data)
-            return self.tokenizer(input_data, return_tensors="pt").to(self.device)
-
-    def _generate(self, inputs, gen_config: GenerationConfig) -> str:
-        input_ids = inputs["input_ids"]
-        input_len = input_ids.shape[-1]
-
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                generation_config=gen_config,
-                pad_token_id=self.tokenizer.pad_token_id,
-                eos_token_id=self.tokenizer.eos_token_id
-            )
-
-        generated_tokens = outputs[0][input_len:]
-        decoded_output = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
-        return decoded_output.strip()
-
-    def _create_generation_config(self, request) -> GenerationConfig:
-        def get_param(name, default=None, cast_type=None):
-            val = self._get_input_scalar(request, name, default)
-            if val is not None and cast_type:
-                return cast_type(val)
-            return val
-
-        return GenerationConfig(
-            max_length=get_param("max_length", 1024, int),
-            max_new_tokens=get_param("max_new_tokens", 256, int),
-            temperature=get_param("temperature", 1.0, float),
-            do_sample=get_param("do_sample", False, bool),
-            top_k=get_param("top_k", 50, int),
-            top_p=get_param("top_p", 1.0, float),
-            repetition_penalty=get_param("repetition_penalty", 1.0, float),
-        )
-
-    def _create_response(self, output_text: str, request_id: str):
-        """생성된 텍스트를 Triton Response 객체로 변환"""
-        output_tensor = pb_utils.Tensor(
-            "text_output",
-            np.array([output_text.encode('utf-8')], dtype=np.bytes_)
-        )
-        return pb_utils.InferenceResponse(output_tensors=[output_tensor])
-
-    def _get_config_param(self, key: str, default: str = None) -> str:
-        params = self.model_config.get('parameters', {})
-        if key in params:
-            return params[key].get('string_value', default)
-        return default
-
-    def _get_input_scalar(self, request, name: str, default=None):
-        tensor = pb_utils.get_input_tensor_by_name(request, name)
-        if tensor is None:
-            return default
-        return self._np_decoder(tensor.as_numpy()[0])
-
-    def _np_decoder(self, obj):
-        if isinstance(obj, bytes):
-           return obj.decode('utf-8')
-        if np.issubdtype(obj, np.integer):
-           return int(obj)
-        if np.issubdtype(obj, np.floating):
-           return round(float(obj), 3)
-        if isinstance(obj, np.bool_):
-           return bool(obj)
-
-    def finalize(self):
-        self.logger.log_info(f"Finalizing model {self.model_name}")
-        self.model = None
-        self.tokenizer = None
-        torch.cuda.empty_cache()
--- a/README.md
+++ b/README.md
@ -0,0 +1,66 @@
+---
+license: apache-2.0
+datasets:
+- cerebras/SlimPajama-627B
+- bigcode/starcoderdata
+- HuggingFaceH4/ultrachat_200k
+- HuggingFaceH4/ultrafeedback_binarized
+language:
+- en
+widget:
+  - example_title: Fibonacci (Python)
+    messages:
+    - role: system
+      content: You are a chatbot who can help code!
+    - role: user
+      content: Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.
+---
+<div align="center">
+
+# TinyLlama-1.1B
+</div>
+
+https://github.com/jzhang38/TinyLlama
+
+The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. 
+
+
+We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
+
+#### This Model
+This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. 
+We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4." 
+
+
+#### How to use
+You will need the transformers>=4.34
+Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
+
+```python
+# Install transformers from source - only needed for versions <= v4.34
+# pip install git+https://github.com/huggingface/transformers.git
+# pip install accelerate
+
+import torch
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
+
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who always responds in the style of a pirate",
+    },
+    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+# <|system|>
+# You are a friendly chatbot who always responds in the style of a pirate.</s>
+# <|user|>
+# How many helicopters can a human eat in one sitting?</s>
+# <|assistant|>
+# ...
+```
--- a/config.json
+++ b/config.json
@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 22,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}
--- a/config.pbtxt
+++ b/config.pbtxt
@ -1,131 +0,0 @@
-# Triton Backend for TransformerLLM.
-backend: "python"
-max_batch_size: 0
-
-# Triton should expect as input a single string
-# input of variable length named 'text_input'
-input [
-
-  {
-    name: "text_input"
-    data_type: TYPE_STRING
-    dims: [ 1 ]
-
-
-  },
-  {
-    name: "max_length"
-    data_type: TYPE_INT32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "max_new_tokens"
-    data_type: TYPE_INT32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "do_sample"
-    data_type: TYPE_BOOL
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "top_k"
-    data_type: TYPE_INT32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "top_p"
-    data_type: TYPE_FP32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "temperature"
-    data_type: TYPE_FP32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "repetition_penalty"
-    data_type: TYPE_FP32
-    dims: [ 1 ]
-
-    optional: true
-
-
-  },
-  {
-    name: "stream"
-    data_type: TYPE_BOOL
-    dims: [ 1 ]
-
-    optional: true
-
-
-  }
-
-]
-
-
-# Triton should expect to respond with a single string
-# output of variable length named 'text_output'
-output [
-
-  {
-    name: "text_output"
-    data_type: TYPE_STRING
-    dims: [ 1 ]
-
-  }
-
-]
-
-parameters: [
-  {
-    key: "base_model_path",
-    value: {string_value: "/cheetah/input/model/groupuser/TinyLlama-1.1B-Chat-v1.0"}
-  },
-  {
-    key: "is_adapter_model",
-    value: {string_value: "false"}
-  },
-  {
-    key: "adapter_model_path",
-    value: {string_value: ""}
-  },
-  
-  {
-    key: "quantization",
-    value: {string_value: "none"}
-  }
-]
-
-instance_group [
-  {
-    kind: KIND_AUTO
-    count: 1
-  }
-]
-
--- a/eval_results.json
+++ b/eval_results.json
@ -0,0 +1,16 @@
+{
+    "epoch": 3.0,
+    "eval_logits/chosen": -2.707406759262085,
+    "eval_logits/rejected": -2.656524419784546,
+    "eval_logps/chosen": -370.1297607421875,
+    "eval_logps/rejected": -296.0738525390625,
+    "eval_loss": 0.513750433921814,
+    "eval_rewards/accuracies": 0.738095223903656,
+    "eval_rewards/chosen": -0.02744222804903984,
+    "eval_rewards/margins": 1.0087225437164307,
+    "eval_rewards/rejected": -1.03616464138031,
+    "eval_runtime": 93.5908,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 21.37,
+    "eval_steps_per_second": 0.673
+}
--- a/generation_config.json
+++ b/generation_config.json
@ -0,0 +1,7 @@
+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 2048,
+  "pad_token_id": 0,
+  "transformers_version": "4.35.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer.model
+++ b/tokenizer.model
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@ -0,0 +1,40 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}