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Model: hachi-intelligence/HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1 Source: Original Platform
2026-06-22 17:56:21 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+base_model: 
+- sbintuitions/sarashina2.2-0.5b-instruct-v0.1
+library_name: transformers
+task_categories:
+- text-generation
+language:
+- ja
+tags:
+- HACHI-Intelligence
+- extractive-summarization
+- SLM
+datasets:
+- hachi-intelligence/JapaneseSummarization-FW2EduJa-Distill
+model_type: causal-lm
+pipeline_tag: text-generation
+finetuned_from: sbintuitions/sarashina2.2-0.5b-instruct-v0.1
+---
+
+# HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1 
+
+For a comprehensive deep dive into the model's architecture, training methodology, and performance benchmarks, please follow the pawprints to our [technical report (available in Japanese)](https://zenn.dev/hachi_intelli/articles/f72205c178f133). 🐕🐾
+
+## Overview
+**HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1** is a commercially viable Japanese Small Language Model (SLM) optimized for high-performance **extractive summarization**. It is specifically designed for professional sectors where factual integrity is non-negotiable, such as **legal affairs, research, healthcare, and finance**.
+
+Built upon SB Intuitions' **sarashina2.2-0.5b-instruct-v0.1**, this model utilizes a knowledge distillation approach to inherit advanced processing capabilities from frontier LLMs while maintaining a lightweight footprint.
+
+### Key Features and Use Cases
+* **High-Fidelity Extractive Summarization**: Expertly preserves proper nouns, numerical data, units, chronological order, and causal relationships within reports and articles.
+* **Optimized for Edge Deployment**: With only 0.5B parameters, it enables rapid inference and integration into resource-constrained environments or edge devices.
+* **Commercial Readiness**: Released under the Apache-2.0 license, providing a transparent and reliable AI foundation for commercial applications, modifications, and redistribution.
+
+## Evaluation
+
+### Benchmarking Methodology
+Traditional benchmarks like **XLSUM-ja** primarily measure "abstractive summarization" (paraphrasing based on context). For models like HACHI-Summary-Ja, which prioritize the precise transcription of original data points, standard abstractive scores may not fully reflect their utility.
+
+To address this, we developed **HES-Ja (HACHI-Extractive-Summarization-Ja)**. We extracted 100 test cases from XLSUM and created a new evaluation set focused on the exact preservation of proper nouns, numerical values (including character-specific notations), and logical flow.
+
+> [!NOTE]
+> Detailed articles and the full HES-Ja dataset are scheduled for public release in the near future.
+
+### Benchmark Results
+
+#### XLSUM-ja Evaluation (Abstractive Metrics)
+Performance in general summarization tasks. The model shows significant strength when specific character constraints are applied.
+
+| Model | BLEU | ROUGE-2 | ROUGE-L |
+| :--- | :---: | :---: | :---: |
+| Qwen3-0.6B | 0.0147 | 0.0419 | 0.0837 |
+| gemma-3-270m-it | 0.0328 | **0.0788** | 0.1549 |
+| granite-4.0-350m | 0.0343 | 0.0728 | 0.1575 |
+| sarashina2.2-0.5b-instruct-v0.1 (Base) | 0.0263 | 0.0731 | 0.1317 |
+| HACHI-Summary-Ja | 0.0276 | 0.0747 | 0.1363 |
+| **HACHI-Summary-Ja (approx. 100 chars)** | **0.0412** | 0.0748 | **0.2055** |
+
+#### HES-Ja Evaluation (Extractive Metrics)
+This benchmark measures the accuracy of information transcription. **HACHI-Summary-Ja outperforms all other tested SLMs across every metric.**
+
+| Model | BLEU | ROUGE-2 | ROUGE-L |
+| :--- | :---: | :---: | :---: |
+| Qwen3-0.6B | 0.0963 | 0.1496 | 0.1820 |
+| gemma-3-270m-it | 0.1960 | 0.3029 | 0.3403 |
+| granite-4.0-350m | 0.1003 | 0.1963 | 0.2330 |
+| sarashina2.2-0.5b-instruct-v0.1 (Base) | 0.2457 | 0.3394 | 0.3635 |
+| **HACHI-Summary-Ja** | **0.2757** | **0.3644** | **0.4044** |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# ===== settings =====
+
+# "","三行","100", "300", "500"から要約条件を設定
+SUMMARY_MODE = ""
+
+# 要約したいテキスト
+TEXT = """忠犬ハチ公
+
+犬種は秋田犬（あきたいぬ）で、性別はオス。名前はハチ。ハチ公の愛称でも知られる。
+ハチが飼い主を待ち続けた渋谷駅の出入り口の前には、ハチの銅像が設置されており、この「忠犬ハチ公像」は、渋谷のシンボルとして、観光名所としても有名である。
+
+ハチは、飼い主が死去した後も駅前で帰りを待ち続けた「忠犬」として知られる。東京・渋谷をはじめ、ゆかりの地には像が置かれている。
+特に、渋谷駅前のハチ公銅像は、いつしか待ち合わせの目印として使われるようになり、その銅像周囲は待ち合わせ場所としては「ハチ公前」などと呼ばれ、広く親しまれている。
+
+ハチの飼い主は、東京府豊多摩郡渋谷町大向（現・東京都渋谷区松濤一丁目）に住んでいた、東京帝国大学の教授・上野英三郎であった。
+彼は、大変な愛犬家であり、ハチの前にもたくさんの犬を飼っていた。出かける時には、渋谷駅までハチを伴うことも多かった。
+しかしながら、ハチを飼い始めた翌年にあたる1925年（大正14年）5月21日に上野は急死した。
+
+上野の死後も、駅前で亡くなった飼い主の帰りを毎日待ち続けたハチの姿は、新聞記事に掲載され、人々に感銘を与えたことから「忠犬ハチ公」と呼ばれるようになった。
+"""
+# 出典:忠犬ハチ公(https://ja.wikipedia.org/wiki/%E5%BF%A0%E7%8A%AC%E3%83%8F%E3%83%81%E5%85%AC)
+
+# ===== main  =====
+
+SUMMARY_PROMPT_MAP = {
+    "100": "原文を100字程度で簡潔に要約してください。",
+    "300": "原文を300字程度で簡潔に要約してください。",
+    "500": "原文を500字程度で簡潔に要約してください。",
+    "三行": "原文を三行で簡潔に要約してください。",
+}
+
+SYSTEM_PROMPT = SUMMARY_PROMPT_MAP.get(
+    SUMMARY_MODE,
+    "原文を簡潔に要約してください。"
+)
+
+model_name = "hachi-intelligence/HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+chat_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer
+)
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": TEXT}
+]
+
+outputs = chat_pipeline(
+    messages,
+    do_sample=False,
+    # temperature=0.1,
+    repetition_penalty=1.00,
+    max_new_tokens=512,
+    num_return_sequences=1,
+)
+
+generated_messages = outputs[0]["generated_text"]
+summary = generated_messages[-1]["content"]
+
+
+# ===== result =====
+print("\n===== SYSTEM PROMPT =====")
+print(SYSTEM_PROMPT)
+print("===== SOURCE TEXT =====")
+print(TEXT,f"({len(TEXT)}文字)")
+print("\n===== SUMMARY =====")
+print(summary,f"({len(summary)}文字)")
+
+# ===== output =====
+# 秋田犬（あきたいぬ）のオス・ハチは、飼い主が死去した後も渋谷駅の出入り口で帰りを待ち続けた「忠犬ハチ公」として知られる。東京・渋谷のハチ公銅像は渋谷のシンボルとして観光名所となり、待ち合わせの目印として「ハチ公前」と呼ばれる。飼い主は東京府豊多摩郡渋谷町大向（現・東京都渋谷区松濤一丁目）の東京帝国大学教授・上野英三郎で、ハチの前に多数の犬を飼っていた。1925年（大正14年）5月21日に上野が死去した後も、ハチは渋谷駅で 飼い主の帰りを待ち続け、新聞記事で「忠犬ハチ公」と称された。 (242文字)
+```
+
+## Acknowledgements
+
+* [SB Intuitions (sarashina2.2-0.5b-instruct-v0.1)](https://huggingface.co/sbintuitions/sarashina2.2-0.5b-instruct-v0.1)
+* [hotchpotch (fineweb-2-edu-japanese)](https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese)
+* [Open-R1](https://github.com/huggingface/open-r1)
+
+## License
+
+This model is licensed under the [Apache-2.0 License](https://www.google.com/search?q=LICENSE).
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,71 @@
+
+{%- set user_messages = messages | selectattr('role', 'equalto', 'user') | list %}
+{%- macro output_available_tools(tools, message) %}
+{%- if tools and (message == user_messages[-1]) %}
+    {{- '<|available_tools|>[' }}
+    {%- for tool in tools %}
+        {%- set tool = tool.function %}
+        {{- "{" }}
+        {%- for key, val in tool.items() if key != "return" %}
+            {%- if val is string %}
+                {{- "'" + key + "': '" + val + "'" }}
+            {%- else %}
+                {{- "'" + key + "': " + val|string }}
+            {%- endif %}
+            {%- if not loop.last %}
+                {{- ", " }}
+            {%- endif %}
+        {%- endfor %}
+        {{- "}" }}
+        {%- if not loop.last %}
+            {{- ", " }}
+        {%- else %}
+            {{- "]" }}
+        {%- endif %}
+    {%- endfor %}
+    {{- eos_token -}}
+{%- endif %}
+{%- endmacro %}
+
+{%- macro output_tool_results(tool_results) %}
+{{- '<|tool_results|>[' }}
+{%- for tool_result in tool_results %}
+    {{- "{'content': " + tool_result.content|string + ", 'call_id': '" + tool_result.call_id + "'}" }}
+{%- endfor %}
+{{- ']' }}
+{{- eos_token -}}
+{%- endmacro %}
+
+{%- macro output_tool_calls(tool_calls) %}
+{{- '<|tool_calls|>[' }}
+{%- for tool_call in tool_calls %}
+    {{- "{'id': '" + tool_call.id + "', 'name': '" + tool_call.name + "', 'arguments': " + tool_call.arguments|string + '}' }}
+{%- endfor %}
+{{- ']' }}
+{%- endmacro %}
+
+{%- for message in messages %}
+    {%- if message['role'] == 'user' %}
+        {%- if tools is defined %}
+            {{- output_available_tools(tools, message) }}
+        {%- endif %}
+        {{- '<|user|>' + message['content'] + eos_token -}}
+    {%- elif message['role'] == 'system' %}
+        {{- '<|system|>' + message['content'] + eos_token -}}
+    {%- elif message['role'] == 'assistant' %}
+        {% set assistant_content = "" %}
+        {%- if message.content is defined %}
+            {% set assistant_content = message.content %}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls -%}
+            {{- '<|assistant|>' + assistant_content + output_tool_calls(message['tool_calls']) + eos_token -}}
+        {%- else %}
+            {{- '<|assistant|>' + assistant_content + eos_token }}
+        {%- endif %}
+    {%- elif message['role'] == 'tool_results' %}
+        {{- output_tool_results(message.tool_results) }}
+    {%- endif %}
+{%- if loop.last and add_generation_prompt -%}
+  {{- '<|assistant|>' -}}
+{%- endif -%}
+{%- endfor %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 80,
+  "hidden_act": "silu",
+  "hidden_size": 1280,
+  "initializer_range": 0.02,
+  "intermediate_size": 4480,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.2",
+  "use_cache": false,
+  "vocab_size": 102400
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 3,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.9,
+  "transformers_version": "4.56.2"
+}
--- a/model.safetensors
+++ b/model.safetensors
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+version https://git-lfs.github.com/spec/v1
+oid sha256:3c360dd7dc13e16447ab5fcdfa486d9e73f86186d1b59ff16b0f1beac5d85bfa
+size 1586121792
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<cls>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<sep>",
+    "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
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:008293028e1a9d9a1038d9b63d989a2319797dfeaa03f171093a57b33a3a8277
+size 1831879
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_bos_token": false,
+  "add_dummy_prefix_space": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+  "tokenizer_class": "LlamaTokenizer",
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+  "use_default_system_prompt": false
+}