Update README.md

README.md

@@ -1,6 +1,15 @@
 ---
 library_name: transformers
-tags: []
+license: cc
+datasets:
+- kinokokoro/ichikara-instruction-003
+language:
+- ja
+metrics:
+- accuracy
+base_model:
+- llm-jp/llm-jp-3-3.7b
+- llm-jp/llm-jp-3-13b
 ---
 
 # Model Card for Model ID
@@ -11,37 +20,41 @@ tags: []
 
 ## Model Details
 
+Final competition report for weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/
+Using finetuning and some other methods to have better result for elyza-tasks-100-TV_0
+With the optimization Technolkogy of Quantamize, PEFT.
+
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+https://drive.google.com/drive/folders/1TcEpKngy72fbxXcu4VxoVUbPfvg8Z1z0
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:Yohei.KObayashi with modification by Hiroshi Hayashi
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Model type: llm-jp/llm-jp
+- **Language(s) (NLP): Japanese
+- **License: CC-BY-NC-SA
+- **Finetuned from model [optional]:Quantamize, PEFT
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+llm-jp-3 1.8B, 3.7B, 13B
+
 
-- **Repository:** [More Information Needed]
+- **Repository:-- [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Learn and get experience to use fine tuning technology and learn how to inplement such fine tuning technologies
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+No intension to be used with such case
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
@@ -51,19 +64,20 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This code is only for students and trainee fo AI implementation.
+Not fully tested for the actual project use case
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This program and updated file is generated by the code by Yohei Kobayashi for training coase by Matsuo-lab @ Tokyo university.
+https://weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/
+Please contact Matsuo-Lab if you plan to use this code and any files related to this project.
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Any students who tries using LLM, this is very useful to understand and get started fromthe perspective of academic perpose
 
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
@@ -71,19 +85,117 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel
+import torch
+from tqdm import tqdm
+import json
+
+HF_TOKEN = "Hugging Face Token"
+
+model_id = ""   # < Model folder path
+adapter_id = "" # Hugging Face ID
+
+# QLoRA config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token = HF_TOKEN
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
+
+model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
+
+datasets = []
+with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
+    item = ""
+    for line in f:
+      line = line.strip()
+      item += line
+      if item.endswith("}"):
+        datasets.append(json.loads(item))
+        item = ""
+results = []
+for data in tqdm(datasets):
+
+  input = data["input"]
+  prompt = f"""### Direction
+  {input}
+  ### Answers
+  """
+
+  input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
+  outputs = model.generate(**input_ids, max_new_tokens=512, do_sample=False, repetition_penalty=1.2,)
+  output = tokenizer.decode(outputs[0][input_ids.input_ids.size(1):], skip_special_tokens=True)
+
+  results.append({"task_id": data["task_id"], "input": input, "output": output})        
+
+results = []
+for data in tqdm(datasets):
+
+  input = data["input"]
+
+  prompt = f"""### 指示
+  {input}
+  ### 回答
+  """
+
+  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+  attention_mask = torch.ones_like(tokenized_input)
+  with torch.no_grad():
+      outputs = model.generate(
+          tokenized_input,
+          attention_mask=attention_mask,
+          max_new_tokens=100,
+          do_sample=False,
+          repetition_penalty=1.2,
+          pad_token_id=tokenizer.eos_token_id
+      )[0]
+  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+
+  results.append({"task_id": data["task_id"], "input": input, "output": output})
+
+import re
+jsonl_id = re.sub(".*/", "", adapter_id)
+with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters
+        f.write('\n')
+
+  
 ## Training Details
 
+Used "Ichikara Instruction"
+ichikara-instruction-003-001-1.json
+
+
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/
 
-[More Information Needed]
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+PEFT
+LoRA rank : 16
+Scaling factor : lora_alpha 32
+Dropout ratio : 0.05
+No Bias
+
 
 #### Preprocessing [optional]
 
@@ -92,35 +204,36 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+36:53
+864/864 
+Epoch 0/1
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+elyza-tasks-100-TV_0.jsonl
 
 ### Testing Data, Factors & Metrics
 
+elyza-tasks-100 with latest TV and TV show related information
+
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
 
 [More Information Needed]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
 [More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+accuracy with limiteation of model execution time
 
 [More Information Needed]
 
@@ -134,13 +247,14 @@ Use the code below to get started with the model.
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
+
 
 [More Information Needed]
 
 ## Environmental Impact
+CPU memory : 48GB
+GPU: L4 (24G)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
@@ -166,11 +280,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Software
 
-[More Information Needed]
+Python 3.10.6
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**