{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Hesam Sheikh",
  "title_listed": "Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/create-a-synthetic-dataset-using-llama-3-1-405b-for-instruction-fine-tuning-9afc22fb6eef/",
  "publish_date": "2024-08-07",
  "corpus_stage": "",
  "notes": "",
  "id": "166348641d90",
  "collected_at": "2026-10-02T06:22:43",
  "title": "Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM\nUsing the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a synthetic dataset for instruction fine-tuning.\n[Hesam Sheikh](https://towardsdatascience.com/author/itshesamsheikh/)\nAugust 7, 20249 min read\n![Created by AI using Leonardo.AI](../images/000.jpeg)\nCreated by AI using Leonardo.AI\nData is the heart of AI and while it is a valuable asset, we know how challenging and costly it is to develop high-quality datasets. A well-curated and filtered dataset can make up for a lack of complexity in a model. This is also the case with Large Language Models where smaller-sized models have shown to outperform bigger LLMs by leveraging good data.\nIn this article, we will explore how to use **Llama 3.1 405B** to create a synthetic dataset of **git commands in natural language**. I will show how you can use this 405B beast without running tens of GPUs in parallel. After having an initial dataset of instructions and responses, we will use **Nvidia's Nemotron 4** as a reward model to filter out any bad prompt/response pairs. Finally, we will push this dataset to HuggingFace for later fine-tuning of our LLM.\nThis will be fast, free, and will leave you much in control.\nI will keep this post concise and knowledge-packed, so make sure to **read through the end** and familiarize yourself with this essential skill.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science), [AI Engineer](https://roadmap.sh/ai-engineer) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## 🦙 Why Llama 3.1\nMeta has gained a firm foothold with the release of their latest family of LLMs, [Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f). The new family includes an upgraded version of the previous 8B and 70B models with increased reasoning abilities and a giant 405B model.\n![Llama 3.1 405 has been successful in reaching nearly the benchmark of the best closed-source models. \\(diagram by Maxime Labonne, with permission\\)](../images/001.jpg)\nLlama 3.1 405 has been successful in reaching nearly the benchmark of the best closed-source models. (diagram by [Maxime Labonne](https://towardsdatascience.com/create-a-synthetic-dataset-using-llama-3-1-405b-for-instruction-fine-tuning-9afc22fb6eef/None), with permission)\nLlama 3.1 405B isn't just impressive in terms of the sheer scale, but also by closing the gap between closed-source and open-source models, more than ever before (above figure).\nThis capability of the 405B model makes it ideal for some of the most important and nuanced workflows, such as Retrieval-Augmented Generation (RAG), supervised fine-tuning (SFT), and most importantly **synthetic data generation**.\n### Why Synthetic Data?\nSynthetic data is created using an artificial model by reproducing the characteristics and features of real-world data. At some point, you will need to work with it _when you need more data than you have_.\nOur example of a dataset of git commands in natural language can show this perfectly. If we want to create an application that takes as input, what the user needs and then suggests the right git command for it, then at the heart of this application we will need an expert LLM. We could use GPT-4o or Claude and will most likely get good results. But there is the problem of the cost. So the alternative would be to **fine-tune** a Small Language Model (SML) such as Llama 3.1 8B or Gemma 2 2B (which I will get to in a later post).\nAnd guess what we need for fine-tuning... Data!\nSince I didn't find the right dataset for this task, we are left with only one solution: to create our dataset **synthetically** using Llama 3.1 405B.\n## 🛠 ️Building the Dataset\nTo build a synthetic dataset using AI, we will use the following outline. You can choose any other LLMs from what I have chosen.\n![Our outline of creating a synthetic dataset. \\(by author\\)](../images/002.png)\nOur outline of creating a synthetic dataset. (by author)\n### Setting Up the API Key\nWe will use the **Nvidia NIM API** to leverage these big LLMs without the hassle of running them locally. Running a model like Llama 3.1 405B on the device would normally require multiple H100 GPUs and unless you work in an organization with such resources, you need to use external APIs.\nTo access your free Nvidia credits, go to [Llama 3.1 on Nvidia NIM](https://build.nvidia.com/explore/discover#llama-3_1-405b-instruct), and click on **Get API Key**. This is what we will use in our code or a `.env` file. Once we have the API, we can set up our connection to the Nvidia server to use the models remotely.\npython\n\n```\nclient = OpenAI(\n    base_url=\"https://integrate.api.nvidia.com/v1\",\n    api_key=os.environ[\"NVIDIA_API_KEY\"]\n)\nMODEL = \"meta/llama-3.1-405b-instruct\"\n```\n\n### Generate Subtopics\nIdeally, we like our dataset to cover various scenarios and situations as much as possible. One way to ensure this is to define **subtopics** and ask Llama 3.1 to provide instructions/response pairs for each of the subtopics. We can choose these subtopics ourselves or leave it to the LLM to decide. I took the second approach in the following code snippet.\npython\n\n```\nn_subtopics = 5\n\nTOPIC_GENERATION_PROMPT_TEMPLATE = \"\"\"\nI want to create a synthetic dataset of natural language and Git commands. Based on this context, give me {n_subtopics} subtopics\nto cover what needs to be covered when working with Git. \n\nThe list must be without numbers, and without any description of the subtopics. The subtopics should be separated by a comma. There must be no other text than the list.\n\"\"\"\n\ndef generate_subtopics(client, n_subtopics):\n    prompt = TOPIC_GENERATION_PROMPT_TEMPLATE.format(n_subtopics=n_subtopics)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n    )\n    return response\n\nresponses = generate_subtopics(client, n_subtopics=n_subtopics)\nprint(responses.choices[0].message.content)\n```\n\nThe LLM suggests five topics: Branching, Merging, Committing, Remote repositories, and Resolving conflicts. It seems like a fair selection of subjects to cover.\n### Generate Instructions\nHaving five subtopics of working with Git, we need Llama 3.1 to generate a set of instructions (or prompts) regarding each of the subtopics. I have asked for one hundred instructions per topic, so ideally, I should get 500 prompts.\nOne thing to keep in mind is that when asking for _N_ number of instructions: it is rare that the model would return exactly as many as you want, even a big model like this.\nEventually, I got a total of 335 instructions for 5 subtopics, which is very different from 500. There are methods to ensure this doesn't happen but for the sake of simplicity, we won't dwell on this.\npython\n\n```\nn_instructions = 100\n\nINSTRUCTION_PROMPT_TEMPLATE = \"\"\"\nThe objective is to create a dataset of user instructions in natural language that should be returned by Git commands.\nGiven a topic in Git, generate {n_instructions} possible concise instructions that could be given to an AI assitant about that topic.\nWrite some of these instructions as if given by someone with limited knowledge of Git terminologies and knowledge, \nlike a beginner programmer. Your response should be in a list format.\n\nThe topic is: {sub_topic}\nThe list must be without numbers. The questions/instructions should be separated by a newline character. There must be no other text than the list.\n\"\"\"\nsubtopic_list = responses.choices[0].message.content.split(\",\")\ndef generate_instructions(client, sub_topic, n_instructions):\n    print(f\"Generating Instructions for {sub_topic}.\")\n    prompt = INSTRUCTION_PROMPT_TEMPLATE.format(sub_topic=sub_topic, n_instructions=n_instructions)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n    )\n    return response.choices[0].message.content\n\ndef instructions_generator(client, subtopic_list, n_instructions):\n    instruction_list = [generate_instructions(client, subtopic, n_instructions) for subtopic in subtopic_list]\n    return instruction_list\n\ninstruction_list = instructions_generator(client, subtopic_list, n_instructions)\n\ninstruction_list_formatted = []\nfor instruction_set in instruction_list:\n    instruction_list_formatted.extend([instruction.strip() for instruction in instruction_set.split(\"n\") if instruction])\nprint(instruction_list_formatted)\n```\n\nHere are some examples of the generated instructions:\nvbnet\n\n```\n'Make a branch that I can merge back into the main branch','I want to make a branch that is based on an older version of the code','Can you show me a log of all commits that have been made to the repository this year?',\n```\n\n### Response Generation\nFor each of the provided instructions, we will also ask for a response. As you can see in the following code snippet, I have specifically asked my responses to be _on-topic, informative, and concise_. By the end, I will have a list of `instruction` and `response` pairs.\npython\n\n```\nRESPONSE_PROMPT_TEMPLATE = \"\"\"\nGiven an question/instruction related to Git, generate a response that could be given. \nKeep the response on-topic, informative, concise.\n\nThe user prompt is: {instruction}\n\"\"\"\ndef generate_responses(client, instruction):\n    prompt = RESPONSE_PROMPT_TEMPLATE.format(instruction=instruction)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n        max_tokens=1024,\n    )\n    return response.choices[0].message.content\n\ndef response_generator(client, instruction_list):\n    response_list = [generate_responses(client, instruction) for instruction in instruction_list]\n    return response_list\n\ninstruction_response_list = response_generator(client, instruction_list_formatted)\ninstruction_response_pair_list = []\nfor instruction, response in zip(instruction_list_formatted, instruction_response_list):\n    instruction_response_pair_list.append(\n        {\n            \"instruction\": instruction,\n            \"responses\": response,\n        }\n    )\n```\n\nswift\n\n```\n{\"instruction\": \"Can you make a branch for me and name it \"new-feature\"\", \n\"responses\": \"To create a new branch named \"new-feature\", you can use the following Git command:nn`git branch new-feature`nnThis will create a new branch with the specified name. If you want to switch to the new branch immediately, you can use:nn`git checkout -b new-feature`nnThis will create the branch and switch to it in one step.\"}\n```\n\n### Filtering Responses with Nemotron 4\nEven though we have our instruction/response pairs, not all of the responses are high-quality. They may be verbose, complex, or false. This is where Nvidia's [**Nemotron 4 340B Reward**](https://huggingface.co/nvidia/Nemotron-4-340B-Reward) model comes into play. It is made exactly for our use case, as according to Nvidia, it _\"can be used as part of a synthetic data generation pipeline to create training data that helps researchers and developers build their own LLMs.\"_\n![Example use of Nemotron 4. \\(by author\\)](../images/003.png)\nExample use of Nemotron 4. (by author)\nWe will give each one of our instruction/response pairs to Nemotron 4, and receive five scores ranging from 0 to 4. These five scores are _helpfulness, correctness, coherence, complexity, and verbosity_. To use the model, I will first define a simple function to feed an instruction and a response to the model and receive the five scores in the shape of a dict.\npython\n\n```\ndef get_scores_from_response(score_response_template):\n    logprobs = score_response_template.choices[0].logprobs.content\n    score_dict = {}\n    for score in logprobs:\n        score_dict[score.token] = score.logprob\n    return score_dict\n\ndef get_response_and_scores(client, model, question, response_content):\n    messages = [\n        {\n            \"role\": \"user\",\n            \"content\": question\n        },\n        {\n            \"role\": \"assistant\",\n            \"content\": response_content\n        }\n    ]\n    response = client.chat.completions.create(\n        model=model,\n        messages=messages,\n    )\n    scores = get_scores_from_response(response)\n    return scores\n```\n\nAfter we have a score for each of the rows in our dataset, we can filter the dataset using each of the five provided criteria. I will filter out bad responses based on **helpfulness** and **verbosity** , as I want to keep my responses concise and informative.\npython\n\n```\nhelpfulness_THRESHOLD = 3\nverbosity_THRESHOLD = 2.5\nsynthetic_data = [data for i, data in enumerate(synthetic_data) \n                  if not (score_list[i][\"helpfulness\"] < helpfulness_THRESHOLD or \n                          score_list[i][\"verbosity\"] > verbosity_THRESHOLD)]\n```\n\n### Push the Dataset to HuggingFace\nFinally, once you have the finished dataset, it's a good practice to push it to HuggingFace to use it later or to share it with other developers. To do this, first log in to HuggingFace and provide a **token** , following the provided link on the login page.\ncsharp\n\n```\nfrom huggingface_hub import login\nlogin()\n```\n\nThen you can load the saved dataset and upload it on your HuggingFace page.\nkotlin\n\n```\nwith open(f'synthetic_data_filtered.jsonl', 'r') as f:\n    data = [json.loads(line) for line in f]\ndataset = Dataset.from_list(data)\ndataset_dict = DatasetDict({\"train\": dataset})\ndataset_dict.push_to_hub(\"hesamsheikh/git-prompt\")\n```\n\nCongrats 🏆 ! So far you have been able to use Llama 3.1 to create a dataset of instructions and responses, and Nemotron 4 to refine the dataset and filter out bad responses. In the end, we saw how easy it is to push the dataset to HuggingFace with no effort. [Create Synthetic Dataset from 1 TOPIC for Instruction Finetuning](https://www.youtube.com/watch?v=FAdRMVAWiak) is also a great inspiration for this article and I would suggest you watch it if you like this topic.\nHere is also the **repository** where you can find the complete code I have used. Don't forget to **star** ⭐the repo if you check it out.\n_[**Creating Synthetic Dataset Using Llama 3.1 405B and Nemotron 4**](https://github.com/hesamsheikh/dataset_git_commands)_\n_**Thank you**_ _for reading through the article!_ Please share your opinions and suggestions if you think any modifications are required.\n### Let's Connect!\n_Subscribe for FREE to be notified of new articles! 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM\nUsing the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a synthetic dataset for instruction fine-tuning.\n[Hesam Sheikh](https://towardsdatascience.com/author/itshesamsheikh/)\nAugust 7, 20249 min read\n![Created by AI using Leonardo.AI](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1EXZMdt2H9Sj4N_k0zIISoA-scaled.jpeg)\nCreated by AI using Leonardo.AI\nData is the heart of AI and while it is a valuable asset, we know how challenging and costly it is to develop high-quality datasets. A well-curated and filtered dataset can make up for a lack of complexity in a model. This is also the case with Large Language Models where smaller-sized models have shown to outperform bigger LLMs by leveraging good data.\nIn this article, we will explore how to use **Llama 3.1 405B** to create a synthetic dataset of **git commands in natural language**. I will show how you can use this 405B beast without running tens of GPUs in parallel. After having an initial dataset of instructions and responses, we will use **Nvidia's Nemotron 4** as a reward model to filter out any bad prompt/response pairs. Finally, we will push this dataset to HuggingFace for later fine-tuning of our LLM.\nThis will be fast, free, and will leave you much in control.\nI will keep this post concise and knowledge-packed, so make sure to **read through the end** and familiarize yourself with this essential skill.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science), [AI Engineer](https://roadmap.sh/ai-engineer) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## 🦙 Why Llama 3.1\nMeta has gained a firm foothold with the release of their latest family of LLMs, [Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f). The new family includes an upgraded version of the previous 8B and 70B models with increased reasoning abilities and a giant 405B model.\n![Llama 3.1 405 has been successful in reaching nearly the benchmark of the best closed-source models. \\(diagram by Maxime Labonne, with permission\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/0-4xIbDs8W1W5j67b.jpg)\nLlama 3.1 405 has been successful in reaching nearly the benchmark of the best closed-source models. (diagram by [Maxime Labonne](https://towardsdatascience.com/create-a-synthetic-dataset-using-llama-3-1-405b-for-instruction-fine-tuning-9afc22fb6eef/None), with permission)\nLlama 3.1 405B isn't just impressive in terms of the sheer scale, but also by closing the gap between closed-source and open-source models, more than ever before (above figure).\nThis capability of the 405B model makes it ideal for some of the most important and nuanced workflows, such as Retrieval-Augmented Generation (RAG), supervised fine-tuning (SFT), and most importantly **synthetic data generation**.\n### Why Synthetic Data?\nSynthetic data is created using an artificial model by reproducing the characteristics and features of real-world data. At some point, you will need to work with it _when you need more data than you have_.\nOur example of a dataset of git commands in natural language can show this perfectly. If we want to create an application that takes as input, what the user needs and then suggests the right git command for it, then at the heart of this application we will need an expert LLM. We could use GPT-4o or Claude and will most likely get good results. But there is the problem of the cost. So the alternative would be to **fine-tune** a Small Language Model (SML) such as Llama 3.1 8B or Gemma 2 2B (which I will get to in a later post).\nAnd guess what we need for fine-tuning... Data!\nSince I didn't find the right dataset for this task, we are left with only one solution: to create our dataset **synthetically** using Llama 3.1 405B.\n## 🛠 ️Building the Dataset\nTo build a synthetic dataset using AI, we will use the following outline. You can choose any other LLMs from what I have chosen.\n![Our outline of creating a synthetic dataset. \\(by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1G31VhEga4EIBuOHMSOwgaQ.png)\nOur outline of creating a synthetic dataset. (by author)\n### Setting Up the API Key\nWe will use the **Nvidia NIM API** to leverage these big LLMs without the hassle of running them locally. Running a model like Llama 3.1 405B on the device would normally require multiple H100 GPUs and unless you work in an organization with such resources, you need to use external APIs.\nTo access your free Nvidia credits, go to [Llama 3.1 on Nvidia NIM](https://build.nvidia.com/explore/discover#llama-3_1-405b-instruct), and click on **Get API Key**. This is what we will use in our code or a `.env` file. Once we have the API, we can set up our connection to the Nvidia server to use the models remotely.\npython\n\n```\nclient = OpenAI(\n    base_url=\"https://integrate.api.nvidia.com/v1\",\n    api_key=os.environ[\"NVIDIA_API_KEY\"]\n)\nMODEL = \"meta/llama-3.1-405b-instruct\"\n```\n\n### Generate Subtopics\nIdeally, we like our dataset to cover various scenarios and situations as much as possible. One way to ensure this is to define **subtopics** and ask Llama 3.1 to provide instructions/response pairs for each of the subtopics. We can choose these subtopics ourselves or leave it to the LLM to decide. I took the second approach in the following code snippet.\npython\n\n```\nn_subtopics = 5\n\nTOPIC_GENERATION_PROMPT_TEMPLATE = \"\"\"\nI want to create a synthetic dataset of natural language and Git commands. Based on this context, give me {n_subtopics} subtopics\nto cover what needs to be covered when working with Git. \n\nThe list must be without numbers, and without any description of the subtopics. The subtopics should be separated by a comma. There must be no other text than the list.\n\"\"\"\n\ndef generate_subtopics(client, n_subtopics):\n    prompt = TOPIC_GENERATION_PROMPT_TEMPLATE.format(n_subtopics=n_subtopics)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n    )\n    return response\n\nresponses = generate_subtopics(client, n_subtopics=n_subtopics)\nprint(responses.choices[0].message.content)\n```\n\nThe LLM suggests five topics: Branching, Merging, Committing, Remote repositories, and Resolving conflicts. It seems like a fair selection of subjects to cover.\n### Generate Instructions\nHaving five subtopics of working with Git, we need Llama 3.1 to generate a set of instructions (or prompts) regarding each of the subtopics. I have asked for one hundred instructions per topic, so ideally, I should get 500 prompts.\nOne thing to keep in mind is that when asking for _N_ number of instructions: it is rare that the model would return exactly as many as you want, even a big model like this.\nEventually, I got a total of 335 instructions for 5 subtopics, which is very different from 500. There are methods to ensure this doesn't happen but for the sake of simplicity, we won't dwell on this.\npython\n\n```\nn_instructions = 100\n\nINSTRUCTION_PROMPT_TEMPLATE = \"\"\"\nThe objective is to create a dataset of user instructions in natural language that should be returned by Git commands.\nGiven a topic in Git, generate {n_instructions} possible concise instructions that could be given to an AI assitant about that topic.\nWrite some of these instructions as if given by someone with limited knowledge of Git terminologies and knowledge, \nlike a beginner programmer. Your response should be in a list format.\n\nThe topic is: {sub_topic}\nThe list must be without numbers. The questions/instructions should be separated by a newline character. There must be no other text than the list.\n\"\"\"\nsubtopic_list = responses.choices[0].message.content.split(\",\")\ndef generate_instructions(client, sub_topic, n_instructions):\n    print(f\"Generating Instructions for {sub_topic}.\")\n    prompt = INSTRUCTION_PROMPT_TEMPLATE.format(sub_topic=sub_topic, n_instructions=n_instructions)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n    )\n    return response.choices[0].message.content\n\ndef instructions_generator(client, subtopic_list, n_instructions):\n    instruction_list = [generate_instructions(client, subtopic, n_instructions) for subtopic in subtopic_list]\n    return instruction_list\n\ninstruction_list = instructions_generator(client, subtopic_list, n_instructions)\n\ninstruction_list_formatted = []\nfor instruction_set in instruction_list:\n    instruction_list_formatted.extend([instruction.strip() for instruction in instruction_set.split(\"n\") if instruction])\nprint(instruction_list_formatted)\n```\n\nHere are some examples of the generated instructions:\nvbnet\n\n```\n'Make a branch that I can merge back into the main branch','I want to make a branch that is based on an older version of the code','Can you show me a log of all commits that have been made to the repository this year?',\n```\n\n### Response Generation\nFor each of the provided instructions, we will also ask for a response. As you can see in the following code snippet, I have specifically asked my responses to be _on-topic, informative, and concise_. By the end, I will have a list of `instruction` and `response` pairs.\npython\n\n```\nRESPONSE_PROMPT_TEMPLATE = \"\"\"\nGiven an question/instruction related to Git, generate a response that could be given. \nKeep the response on-topic, informative, concise.\n\nThe user prompt is: {instruction}\n\"\"\"\ndef generate_responses(client, instruction):\n    prompt = RESPONSE_PROMPT_TEMPLATE.format(instruction=instruction)\n    response = client.chat.completions.create(\n        model=MODEL,\n        messages=[\n            {\"role\": \"user\",\n             \"content\": prompt}\n        ],\n        temperature=0.2,\n        top_p=0.7,\n        max_tokens=1024,\n    )\n    return response.choices[0].message.content\n\ndef response_generator(client, instruction_list):\n    response_list = [generate_responses(client, instruction) for instruction in instruction_list]\n    return response_list\n\ninstruction_response_list = response_generator(client, instruction_list_formatted)\ninstruction_response_pair_list = []\nfor instruction, response in zip(instruction_list_formatted, instruction_response_list):\n    instruction_response_pair_list.append(\n        {\n            \"instruction\": instruction,\n            \"responses\": response,\n        }\n    )\n```\n\nswift\n\n```\n{\"instruction\": \"Can you make a branch for me and name it \"new-feature\"\", \n\"responses\": \"To create a new branch named \"new-feature\", you can use the following Git command:nn`git branch new-feature`nnThis will create a new branch with the specified name. If you want to switch to the new branch immediately, you can use:nn`git checkout -b new-feature`nnThis will create the branch and switch to it in one step.\"}\n```\n\n### Filtering Responses with Nemotron 4\nEven though we have our instruction/response pairs, not all of the responses are high-quality. They may be verbose, complex, or false. This is where Nvidia's [**Nemotron 4 340B Reward**](https://huggingface.co/nvidia/Nemotron-4-340B-Reward) model comes into play. It is made exactly for our use case, as according to Nvidia, it _\"can be used as part of a synthetic data generation pipeline to create training data that helps researchers and developers build their own LLMs.\"_\n![Example use of Nemotron 4. \\(by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1okmsL_7dIpvWvzoeZ9HfpA.png)\nExample use of Nemotron 4. (by author)\nWe will give each one of our instruction/response pairs to Nemotron 4, and receive five scores ranging from 0 to 4. These five scores are _helpfulness, correctness, coherence, complexity, and verbosity_. To use the model, I will first define a simple function to feed an instruction and a response to the model and receive the five scores in the shape of a dict.\npython\n\n```\ndef get_scores_from_response(score_response_template):\n    logprobs = score_response_template.choices[0].logprobs.content\n    score_dict = {}\n    for score in logprobs:\n        score_dict[score.token] = score.logprob\n    return score_dict\n\ndef get_response_and_scores(client, model, question, response_content):\n    messages = [\n        {\n            \"role\": \"user\",\n            \"content\": question\n        },\n        {\n            \"role\": \"assistant\",\n            \"content\": response_content\n        }\n    ]\n    response = client.chat.completions.create(\n        model=model,\n        messages=messages,\n    )\n    scores = get_scores_from_response(response)\n    return scores\n```\n\nAfter we have a score for each of the rows in our dataset, we can filter the dataset using each of the five provided criteria. I will filter out bad responses based on **helpfulness** and **verbosity** , as I want to keep my responses concise and informative.\npython\n\n```\nhelpfulness_THRESHOLD = 3\nverbosity_THRESHOLD = 2.5\nsynthetic_data = [data for i, data in enumerate(synthetic_data) \n                  if not (score_list[i][\"helpfulness\"] < helpfulness_THRESHOLD or \n                          score_list[i][\"verbosity\"] > verbosity_THRESHOLD)]\n```\n\n### Push the Dataset to HuggingFace\nFinally, once you have the finished dataset, it's a good practice to push it to HuggingFace to use it later or to share it with other developers. To do this, first log in to HuggingFace and provide a **token** , following the provided link on the login page.\ncsharp\n\n```\nfrom huggingface_hub import login\nlogin()\n```\n\nThen you can load the saved dataset and upload it on your HuggingFace page.\nkotlin\n\n```\nwith open(f'synthetic_data_filtered.jsonl', 'r') as f:\n    data = [json.loads(line) for line in f]\ndataset = Dataset.from_list(data)\ndataset_dict = DatasetDict({\"train\": dataset})\ndataset_dict.push_to_hub(\"hesamsheikh/git-prompt\")\n```\n\nCongrats 🏆 ! So far you have been able to use Llama 3.1 to create a dataset of instructions and responses, and Nemotron 4 to refine the dataset and filter out bad responses. In the end, we saw how easy it is to push the dataset to HuggingFace with no effort. [Create Synthetic Dataset from 1 TOPIC for Instruction Finetuning](https://www.youtube.com/watch?v=FAdRMVAWiak) is also a great inspiration for this article and I would suggest you watch it if you like this topic.\nHere is also the **repository** where you can find the complete code I have used. Don't forget to **star** ⭐the repo if you check it out.\n_[**Creating Synthetic Dataset Using Llama 3.1 405B and Nemotron 4**](https://github.com/hesamsheikh/dataset_git_commands)_\n_**Thank you**_ _for reading through the article!_ Please share your opinions and suggestions if you think any modifications are required.\n### Let's Connect!\n_Subscribe for FREE to be notified of new articles! You can also find me on_[ _LinkedIn_](https://www.linkedin.com/in/hesamsheikh/) _and_[ _Twitter_](https://x.com/itsHesamSheikh) _._\n> [**Get an email whenever Hesam Sheikh publishes.**](https://medium.com/@itshesamsheikh/subscribe)\n## Further Reads\nIf you have reached so far, you might also find these articles interesting:\n> [**What We Still Don't Understand About Machine Learning**](https://towardsdatascience.com/what-we-still-dont-understand-about-machine-learning-699e0002a057)\n> [**A Comprehensive Guide to Collaborative AI Agents in Practice**](https://towardsdatascience.com/a-comprehensive-guide-to-collaborative-ai-agents-in-practice-1f4048947d9c)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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