Model Intelligence Sheet
richarderkhov/azure99_-_blossom-v4-qwen1_5-4b-gguf overview
💻Github • 🚀Blossom Chat Demo ### 介绍 Blossom是一个对话式语言模型,基于Qwen1.5-4B预训练模型,在Blossom Orca/Wizard/Chat/Math混合数据集上进行指令精调得来。Blossom拥有强大的通用能力及上下文理解能力,此外,训练使用的高质量中英文数据集也进行了开源。 训练分为两阶段,第一阶段使用100K Wizard、100K Orca、20K Math单轮指令数据集,训练1个epoch;第二阶段使用50K Blossom chat多轮对话数据集、以及上一阶段中随机采样2%的数据,训练3个epoch。 ### 推理 推理采用对话续写的形式。 单轮对话 多轮对话 注意:在历史对话的Bot输出结尾,拼接一个<|endoftext|>
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0
Pipeline
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Library
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Visibility
Public
Access
Open
Repository Files & Downloads
22 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| blossom-v4-qwen1_5-4b.IQ3_M.gguf | GGUF | IQ3_M | 1.81 GB | Download |
| blossom-v4-qwen1_5-4b.IQ3_S.gguf | GGUF | IQ3_S | 1.73 GB | Download |
| blossom-v4-qwen1_5-4b.IQ3_XS.gguf | GGUF | IQ3_XS | 1.66 GB | Download |
| blossom-v4-qwen1_5-4b.IQ4_NL.gguf | GGUF | IQ4_NL | 2.18 GB | Download |
| blossom-v4-qwen1_5-4b.IQ4_XS.gguf | GGUF | IQ4_XS | 2.08 GB | Download |
| blossom-v4-qwen1_5-4b.Q2_K.gguf | GGUF | Q2_K | 1.51 GB | Download |
| blossom-v4-qwen1_5-4b.Q3_K.gguf | GGUF | Q3_K | 1.89 GB | Download |
| blossom-v4-qwen1_5-4b.Q3_K_L.gguf | GGUF | Q3_K_L | 2.03 GB | Download |
| blossom-v4-qwen1_5-4b.Q3_K_M.gguf | GGUF | Q3_K_M | 1.89 GB | Download |
| blossom-v4-qwen1_5-4b.Q3_K_S.gguf | GGUF | Q3_K_S | 1.73 GB | Download |
| blossom-v4-qwen1_5-4b.Q4_0.gguf | GGUF | — | 2.17 GB | Download |
| blossom-v4-qwen1_5-4b.Q4_1.gguf | GGUF | — | 2.38 GB | Download |
| blossom-v4-qwen1_5-4b.Q4_K.gguf | GGUF | Q4_K | 2.29 GB | Download |
| blossom-v4-qwen1_5-4b.Q4_K_M.gguf | GGUF | Q4_K_M | 2.29 GB | Download |
| blossom-v4-qwen1_5-4b.Q4_K_S.gguf | GGUF | Q4_K_S | 2.18 GB | Download |
| blossom-v4-qwen1_5-4b.Q5_0.gguf | GGUF | — | 2.58 GB | Download |
| blossom-v4-qwen1_5-4b.Q5_1.gguf | GGUF | — | 2.79 GB | Download |
| blossom-v4-qwen1_5-4b.Q5_K.gguf | GGUF | Q5_K | 2.64 GB | Download |
| blossom-v4-qwen1_5-4b.Q5_K_M.gguf | GGUF | Q5_K_M | 2.64 GB | Download |
| blossom-v4-qwen1_5-4b.Q5_K_S.gguf | GGUF | Q5_K_S | 2.58 GB | Download |
| blossom-v4-qwen1_5-4b.Q6_K.gguf | GGUF | Q6_K | 3.03 GB | Download |
| blossom-v4-qwen1_5-4b.Q8_0.gguf | GGUF | — | 3.92 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"frontmatter": {},
"hero_image_url": "",
"summary": "💻Github • 🚀Blossom Chat Demo ### 介绍 Blossom是一个对话式语言模型,基于Qwen1.5-4B预训练模型,在Blossom Orca/Wizard/Chat/Math混合数据集上进行指令精调得来。Blossom拥有强大的通用能力及上下文理解能力,此外,训练使用的高质量中英文数据集也进行了开源。 训练分为两阶段,第一阶段使用100K Wizard、100K Orca、20K Math单轮指令数据集,训练1个epoch;第二阶段使用50K Blossom chat多轮对话数据集、以及上一阶段中随机采样2%的数据,训练3个epoch。 ### 推理 推理采用对话续写的形式。 单轮对话 `` A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions. |Human|: 你好 |Bot|: ` 多轮对话 ` A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions. |Human|: 你好 |Bot|: 你好,有什么我能帮助你的? |Human|: 介绍下中国的首都吧 |Bot|: `` 注意:在历史对话的Bot输出结尾,拼接一个<|endoftext|>",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nblossom-v4-qwen1_5-4b - GGUF\n- Model creator: https://huggingface.co/Azure99/\n- Original model: https://huggingface.co/Azure99/blossom-v4-qwen1_5-4b/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [blossom-v4-qwen1_5-4b.Q2_K.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q2_K.gguf) | Q2_K | 1.51GB |\n| [blossom-v4-qwen1_5-4b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.IQ3_XS.gguf) | IQ3_XS | 1.66GB |\n| [blossom-v4-qwen1_5-4b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.IQ3_S.gguf) | IQ3_S | 1.73GB |\n| [blossom-v4-qwen1_5-4b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q3_K_S.gguf) | Q3_K_S | 1.73GB |\n| [blossom-v4-qwen1_5-4b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.IQ3_M.gguf) | IQ3_M | 1.81GB |\n| [blossom-v4-qwen1_5-4b.Q3_K.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q3_K.gguf) | Q3_K | 1.89GB |\n| [blossom-v4-qwen1_5-4b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q3_K_M.gguf) | Q3_K_M | 1.89GB |\n| [blossom-v4-qwen1_5-4b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q3_K_L.gguf) | Q3_K_L | 2.03GB |\n| [blossom-v4-qwen1_5-4b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.IQ4_XS.gguf) | IQ4_XS | 2.08GB |\n| [blossom-v4-qwen1_5-4b.Q4_0.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q4_0.gguf) | Q4_0 | 2.17GB |\n| [blossom-v4-qwen1_5-4b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.IQ4_NL.gguf) | IQ4_NL | 2.18GB |\n| [blossom-v4-qwen1_5-4b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q4_K_S.gguf) | Q4_K_S | 2.18GB |\n| [blossom-v4-qwen1_5-4b.Q4_K.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q4_K.gguf) | Q4_K | 2.29GB |\n| [blossom-v4-qwen1_5-4b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q4_K_M.gguf) | Q4_K_M | 2.29GB |\n| [blossom-v4-qwen1_5-4b.Q4_1.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q4_1.gguf) | Q4_1 | 2.38GB |\n| [blossom-v4-qwen1_5-4b.Q5_0.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q5_0.gguf) | Q5_0 | 2.58GB |\n| [blossom-v4-qwen1_5-4b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q5_K_S.gguf) | Q5_K_S | 2.58GB |\n| [blossom-v4-qwen1_5-4b.Q5_K.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q5_K.gguf) | Q5_K | 2.64GB |\n| [blossom-v4-qwen1_5-4b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q5_K_M.gguf) | Q5_K_M | 2.64GB |\n| [blossom-v4-qwen1_5-4b.Q5_1.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q5_1.gguf) | Q5_1 | 2.79GB |\n| [blossom-v4-qwen1_5-4b.Q6_K.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q6_K.gguf) | Q6_K | 3.03GB |\n| [blossom-v4-qwen1_5-4b.Q8_0.gguf](https://huggingface.co/RichardErkhov/Azure99_-_blossom-v4-qwen1_5-4b-gguf/blob/main/blossom-v4-qwen1_5-4b.Q8_0.gguf) | Q8_0 | 3.92GB |\n\n\n\n\nOriginal model description:\n---\nlicense: apache-2.0\ndatasets:\n- Azure99/blossom-chat-v2\n- Azure99/blossom-math-v3\n- Azure99/blossom-wizard-v2\n- Azure99/blossom-orca-v2\nlanguage:\n- zh\n- en\npipeline_tag: text-generation\n---\n# **BLOSSOM-v4-qwen1_5-4b**\n\n[💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/)\n\n### 介绍\n\nBlossom是一个对话式语言模型,基于Qwen1.5-4B预训练模型,在Blossom Orca/Wizard/Chat/Math混合数据集上进行指令精调得来。Blossom拥有强大的通用能力及上下文理解能力,此外,训练使用的高质量中英文数据集也进行了开源。\n\n训练分为两阶段,第一阶段使用100K Wizard、100K Orca、20K Math单轮指令数据集,训练1个epoch;第二阶段使用50K Blossom chat多轮对话数据集、以及上一阶段中随机采样2%的数据,训练3个epoch。\n\n### 推理\n\n推理采用对话续写的形式。\n\n单轮对话\n\n```\nA chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.\n|Human|: 你好\n|Bot|: \n```\n\n多轮对话\n\n```\nA chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.\n|Human|: 你好\n|Bot|: 你好,有什么我能帮助你的?<|endoftext|>\n|Human|: 介绍下中国的首都吧\n|Bot|: \n```\n\n注意:在历史对话的Bot输出结尾,拼接一个<|endoftext|>\n\n",
"related_quantizations": []
},
"tags": [
"gguf",
"endpoints_compatible",
"region:us"
],
"likes": 0,
"downloads": 192,
"gated": false,
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"last_modified": "2024-08-19T04:31:24.000Z",
"created_at": "2024-08-19T03:45:32.000Z",
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Source payload excerpt (from Hugging Face API)
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