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README.md

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+---
+base_model:
+- tokyotech-llm/Swallow-70b-instruct-hf
+- allenai/tulu-2-dpo-70b
+tags:
+- mergekit
+- merge
+language:
+  - en
+  - ja
+library_name: transformers
+pipeline_tag: text-generation
+license: llama2
+model_type: llama
+---
+# Superswallow-70b-NVE
+
+**Important Notice:**
+
+This model partially utilizes the parameters of Tulu V2 DPO finetuned based on Llama 2, so it may inherit the AI2 ImpACT license. Please use the model keeping in mind that there may be changes regarding the license if AI2 contacts me.
+
+The [AI2 ImpACT license](https://allenai.org/impact-license) includes information about data artifacts and model artifacts, but does not cover the case of directly applying parts of the LLM parameters of a model artifact to other models. However, I respect their research and great work, so I will change the license immediately if AI2 contacts me.
+
+## Description
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The model was created by injecting the ability to follow user intent from [Tulu 2 DPO](https://arxiv.org/abs/2311.10702) into the [Swallow](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) instract model.
+
+It was a proof of concept for merging LLMs trained in other languages, and paid close attention to preserving the linguistic capabilities of the merge-based model.
+
+As far as I know, Swallow is the full set Llama 2 model(7B, 13B, 70B) that can output the most beautiful Japanese. Therefore, I used it as the base model for merging this time. Thank you for their wonderful work.
+
+## Test environment
+
+This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `simple-1` and `Null preset` for Generation.
+
+### Recommendation
+
+Use `simple-1` settings:
+- temperature: 0.7
+- top_p: 0.9
+- repetition_penalty: 1.15
+- top_k: 20
+
+### Tested `temperature` Range
+
+- temperature: 0.3 - 1.0
+
+It works fine in most cases, but depending on the prompt, the output may become unstable at the temperature around 1.0.
+
+**If the output does not follow the user intent, please lower the temperature to 0.5 or less.**
+
+### Tested `repetition_penalty` Range
+
+- repetition_penalty: 1.0 - 1.15
+
+It works fine in most cases, but depending on the prompt, the output may become unstable at the repetition_penalty around 1.0.
+
+## Prompt template
+
+All prompt templates are available as well.
+
+### Tulu Style
+
+```
+<|user|>
+Your message here!
+<|assistant|>
+
+```
+
+For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
+
+### Swallow Style (Alpaca format)
+
+```
+以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。リクエストを適切に完了するための回答を記述してください。
+
+### 指示:
+{instruction}
+
+### 応答:
+
+```
+
+## Use the instruct model
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "nitky/Superswallow-70b-NVE"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto", load_in_4bit = True)
+
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。"
+        "リクエストを適切に完了するための回答を記述してください。\n\n"
+        "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:"
+
+    ),
+    "prompt_no_input": (
+        "以下に、あるタスクを説明する指示があります。"
+        "リクエストを適切に完了するための回答を記述してください。\n\n"
+        "### 指示:\n{instruction}\n\n### 応答:"
+    ),
+}
+
+def create_prompt(instruction, input=None):
+    """
+    Generates a prompt based on the given instruction and an optional input.
+    If input is provided, it uses the 'prompt_input' template from PROMPT_DICT.
+    If no input is provided, it uses the 'prompt_no_input' template.
+
+    Args:
+        instruction (str): The instruction describing the task.
+        input (str, optional): Additional input providing context for the task. Default is None.
+
+    Returns:
+        str: The generated prompt.
+    """
+    if input:
+        # Use the 'prompt_input' template when additional input is provided
+        return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input)
+    else:
+        # Use the 'prompt_no_input' template when no additional input is provided
+        return PROMPT_DICT["prompt_no_input"].format(instruction=instruction)
+
+# Example usage
+instruction_example = "以下のトピックに関する詳細な情報を提供してください。"
+input_example = "東京工業大学の主なキャンパ���について教えてください"
+prompt = create_prompt(instruction_example, input_example)
+
+input_ids = tokenizer.encode(
+    prompt,
+    add_special_tokens=False,
+    return_tensors="pt"
+)
+
+tokens = model.generate(
+    input_ids.to(device=model.device),
+    max_new_tokens=200,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.15,
+    top_k=20,
+    do_sample=True,
+)
+
+out = tokenizer.decode(tokens[0], skip_special_tokens=True)
+print(out)
+
+```
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
+
+### Models Merged
+
+The following models were included in the merge:
+* [tokyotech-llm/Swallow-70b-NVE-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-instruct-hf)
+* [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b)
+
+### Configuration
+
+The command example:
+
+```bash
+# please change the path and options according to your environment
+mergekit-mega --cuda Superswallow-70b-NVE.yml ~/text-generation-webui/models
+```
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+models:
+  - model: tokyotech-llm/Swallow-70b-NVE-instruct-hf
+    parameters:
+      weight: 1.0
+  - model: allenai/tulu-2-dpo-70b
+    parameters:
+      weight: 1.0
+merge_method: linear
+dtype: bfloat16
+tokenizer_source: model:tokyotech-llm/Swallow-70b-NVE-instruct-hf
+name: Superswallow-70b-NVE
+```

config.json

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+{
+  "_name_or_path": "nitky/Superswallow-70b-NVE",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 4096,
+  "max_sequence_length": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

mergekit_config.yml

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+models:
+  - model: tokyotech-llm/Swallow-70b-NVE-instruct-hf
+    parameters:
+      weight: 1.0
+  - model: allenai/tulu-2-dpo-70b
+    parameters:
+      weight: 1.0
+merge_method: linear
+dtype: bfloat16
+tokenizer_source: model:tokyotech-llm/Swallow-70b-NVE-instruct-hf
+name: Superswallow-70b-NVE

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