Create README.md

README.md

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+---
+language:
+- ja
+tags:
+- japanese-stablelm
+- causal-lm
+pipeline_tag: text-generation
+base_model: ohwi/japanese-stablelm-instruct-gamma-7b-repro
+datasets:
+- argilla/ultrafeedback-binarized-preferences-cleaned
+- llm-jp/hh-rlhf-12k-ja
+license: apache-2.0
+extra_gated_fields:
+  Name: text
+  Email: text
+  Country: text
+  Organization or Affiliation: text
+  I allow Stability AI to contact me about information related to its models and research: checkbox
+---
+
+
+# Japanese Stable LM Instruct Gamma 7B + DPO
+
+## Model Description
+
+This is a 7B-parameter decoder-only Japanese language model fine-tuned on preference datasets, built on top of the STF model [Japanese Stable LM Instruct Gamma 7B Reproduced](https://huggingface.co/ohwi/japanese-stablelm-instruct-gamma-7b-repro).
+
+This model is trained with [notus](https://github.com/argilla-io/notus) code base.
+
+
+### Training Datasets
+
+- Machine Translated [Ultrafeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
+- [hh-rlhf-12k-ja](https://huggingface.co/datasets/llm-jp/hh-rlhf-12k-ja)
+
+The dataset is machine translated version of `Ultrafeedback` and `hh-rlhf-12k-ja`. Some samples from Ultrafeedback dataset are missing because of API request failure.
+
+
+### Benchmarks
+
+The result is evaluated by [Nejumi-leaderboard Neo](https://github.com/wandb/llm-leaderboard/tree/b2723944d4955768cb93c18ffe162a8ff4e88955).
+
+- llm-jp-eval:
+
+  |AVG    |EL  |FA     |MC   |MR   |NLI   |QA     |RC     |chabsa|jamp |janli|jcommonsenseqa|jemhopqa|jnli |jsem |jsick|jsquad |mawps |niilc |wiki_coreference|wiki_dependency|wiki_ner|wiki_pas|wiki_reading|
+  |-------|----|-------|-----|-----|------|-------|-------|------|-----|-----|--------------|--------|-----|-----|-----|-------|------|------|----------------|---------------|--------|--------|------------|
+  |0.3207 |0.0 |0.1505 |0.81 |0.16 |0.268 |0.1823 |0.6744 |0.0   |0.09 |0.56 |0.81          |0.1546  |0.01 |0.57 |0.11 |0.6744 |0.16  |0.21  |0.0             |0.0            |0.0     |0.0     |0.7525      |
+
+
+- Japanese Mt-Bench:
+
+  |coding|extraction|humanities|math|reasoning|roleplay|stem|writing|
+  |------|----------|----------|----|---------|--------|----|-------|
+  |2.5   |3.7       |3.75      |1.65|3.45     |6.95    |5.3 |7.15   |
+
+
+- Overall Average: 0.3756625
+
+
+---
+
+
+( Below is the original readme of `Japanese Stable LM Instruct Gamma 7B` )
+
+
+<br>
+
+
+# Japanese Stable LM Instruct Gamma 7B
+
+## Model Description
+
+This is a 7B-parameter decoder-only Japanese language model fine-tuned on instruction-following datasets, built on top of the base model [Japanese Stable LM Base Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b).
+
+*If you are in search of a smaller model, please check [Japanese StableLM-3B-4E1T Instruct](https://huggingface.co/stabilityai/japanese-stablelm-3b-4e1t-base/blob/main/README.md).*
+
+## Usage
+
+Ensure you are using Transformers 4.34.0 or newer.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-instruct-gamma-7b")
+model = AutoModelForCausalLM.from_pretrained(
+  "stabilityai/japanese-stablelm-instruct-gamma-7b",
+  torch_dtype="auto",
+)
+model.eval()
+
+if torch.cuda.is_available():
+    model = model.to("cuda")
+
+def build_prompt(user_query, inputs="", sep="\n\n### "):
+    sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
+    p = sys_msg
+    roles = ["指示", "応答"]
+    msgs = [": \n" + user_query, ": \n"]
+    if inputs:
+        roles.insert(1, "入力")
+        msgs.insert(1, ": \n" + inputs)
+    for role, msg in zip(roles, msgs):
+        p += sep + role + msg
+    return p
+
+# Infer with prompt without any additional input
+user_inputs = {
+    "user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
+    "inputs": "情けは人のためならず"
+}
+prompt = build_prompt(**user_inputs)
+
+input_ids = tokenizer.encode(
+    prompt, 
+    add_special_tokens=True, 
+    return_tensors="pt"
+)
+
+tokens = model.generate(
+    input_ids.to(device=model.device),
+    max_new_tokens=256,
+    temperature=1,
+    top_p=0.95,
+    do_sample=True,
+)
+
+out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
+print(out)
+```
+
+## Model Details
+
+* **Developed by**: [Stability AI](https://stability.ai/)
+* **Model type**: `Japanese Stable LM Instruct Gamma 7B` model is an auto-regressive language model based on the transformer decoder architecture.
+* **Language(s)**: Japanese
+* **License**: This model is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
+
+### Model Architecture
+
+For details, please see Mistral AI's [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). 
+
+
+### Training Datasets
+
+- [Japanese translation of the Databricks Dolly-15k dataset](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
+- [Japanese translation of the subset of the Anthropic HH dataset](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k)
+- [Wikinews](https://ja.wikinews.org/wi) [subset](https://huggingface.co/datasets/fujiki/llm-japanese-dataset_wikinews) of the [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset)
+
+
+
+## Use and Limitations
+
+### Intended Use
+
+The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.
+
+### Limitations and bias
+
+The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
+
+## Credits
+
+The fine-tuning was carried out by [Fujiki Nakamura](https://huggingface.co/fujiki).
+Other aspects, including data preparation and evaluation, were handled by the Language Team of Stability AI Japan, notably [Meng Lee](https://huggingface.co/leemeng), [Makoto Shing](https://huggingface.co/mkshing), [Paul McCann](https://huggingface.co/polm-stability), [Naoki Orii](https://huggingface.co/mrorii), and  [Takuya Akiba](https://huggingface.co/iwiwi).
+
+
+## Acknowledgements
+
+This model is based on Mistral-7B-v0.1 released by the Mistral AI team. We are grateful to the Mistral AI team for providing such an excellent base model.
+
+We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
+
+We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.