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--- |
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library_name: transformers |
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tags: |
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- transformer |
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- SFT |
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- Japanese |
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- trl |
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- text-generation-inference |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- llm-jp/llm-jp-3-13b |
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--- |
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# Model Card for Model ID |
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このモデルは東京大学松尾・岩澤研究室のLLM講座2024の最終課題のためにllm-jp/llm-jp-3-13bをファインチューニングして作成したモデルです。 |
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## Model Details |
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### Model Description |
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・Base model：llm-jp/llm-jp-3-13b |
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・Dataset for SFT：ichikara-instruction |
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・Liscence：Apahce2.0 |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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## 推論用コード |
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``` |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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!pip install -q datasets==3.0.2 |
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!pip install transformers==4.45.0 |
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!pip install accelerate==1.0.1 |
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!pip install peft==0.13.2 |
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!pip install trl==0.11.4 |
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!pip install bitsandbytes==0.44.1 |
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!pip install ipywidgets --upgrade |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BitsAndBytesConfig, |
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) |
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from peft import PeftModel |
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import torch |
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from tqdm import tqdm |
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import json |
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HF_TOKEN = "Hugging Face Token" |
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model_id = "llm-jp/llm-jp-3-13b" |
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adapter_id = "ryowatanabe240215/llm-jp-3-13b-finetune1214_2" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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quantization_config=bnb_config, |
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device_map="auto", |
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token = HF_TOKEN |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN) |
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model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN) |
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datasets = [] |
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with open("elyza-tasks-100-TV_0.jsonl", "r") as f: |
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item = "" |
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for line in f: |
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line = line.strip() |
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item += line |
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if item.endswith("}"): |
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datasets.append(json.loads(item)) |
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item = "" |
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results = [] |
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for data in tqdm(datasets): |
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input = data["input"] |
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prompt = f"""以下は、タスクを説明する指示と、指示に対する回答の組み合わせです。指示を適切に満たす回答を書きなさい。 |
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### 指示 |
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{input} |
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### 回答 |
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""" |
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tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) |
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attention_mask = torch.ones_like(tokenized_input) |
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with torch.no_grad(): |
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outputs = model.generate( |
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tokenized_input, |
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attention_mask=attention_mask, |
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max_new_tokens=100, |
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do_sample=False, |
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repetition_penalty=1.2, |
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pad_token_id=tokenizer.eos_token_id |
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)[0] |
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output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True) |
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results.append({"task_id": data["task_id"], "input": input, "output": output}) |
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import re |
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jsonl_id = re.sub(".*/", "", adapter_id) |
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with open(f"outputs.jsonl", 'w', encoding='utf-8') as f: |
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for result in results: |
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json.dump(result, f, ensure_ascii=False) |
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f.write('\n') |
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``` |
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