Add OLLaMA doc (modelscope#1660)

docs/source/LLM/OLLAMA导出文档.md

@@ -0,0 +1,154 @@
+# OLLaMA导出文档
+
+SWIFT已经支持了OLLaMA Modelfile的导出能力，该能力合并到了`swift export`命令中。
+
+## 目录
+
+- [环境准备](#环境准备)
+- [导出](#导出)
+- [需要注意的问题](#需要注意的问题)
+
+## 环境准备
+
+```shell
+# 设置pip全局镜像 (加速下载)
+pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
+# 安装ms-swift
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install -e '.[llm]'
+```
+
+OLLaMA导出不需要其他模块支持，因为SWIFT仅会导出ModelFile，后续的运行用户可以自行处理。
+
+## 导出
+
+OLLaMA导出命令行如下：
+
+```shell
+# model_type
+swift export --model_type llama3-8b-instruct --to_ollama true --ollama_output_dir llama3-8b-instruct-ollama
+# ckpt_dir，注意lora训练需要增加--merge_lora true
+swift export --ckpt_dir /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942 --to_ollama true --ollama_output_dir qwen-7b-chat-ollama --merge_lora true
+```
+
+执行后会打印如下log：
+```shell
+[INFO:swift] Exporting to ollama:
+[INFO:swift] If you have a gguf file, try to pass the file by :--gguf_file /xxx/xxx.gguf, else SWIFT will use the original(merged) model dir
+[INFO:swift] Downloading the model from ModelScope Hub, model_id: LLM-Research/Meta-Llama-3-8B-Instruct
+[WARNING:modelscope] Authentication has expired, please re-login with modelscope login --token "YOUR_SDK_TOKEN" if you need to access private models or datasets.
+[WARNING:modelscope] Using branch: master as version is unstable, use with caution
+[INFO:swift] Loading the model using model_dir: /mnt/workspace/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct
+[INFO:swift] Save Modelfile done, you can start ollama by:
+[INFO:swift] > ollama serve
+[INFO:swift] In another terminal:
+[INFO:swift] > ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/llama3-8b-instruct-ollama/Modelfile
+[INFO:swift] > ollama run my-custom-model
+[INFO:swift] End time of running main: 2024-08-09 17:17:48.768722
+```
+
+提示可以运行，此时打开ModelFile查看：
+
+```text
+FROM /mnt/workspace/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct
+TEMPLATE """{{ if .System }}<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ else }}<|begin_of_text|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}<|eot_id|>"""
+PARAMETER stop "<|eot_id|>"
+PARAMETER temperature 0.3
+PARAMETER top_k 20
+PARAMETER top_p 0.7
+PARAMETER repeat_penalty 1.0
+```
+
+用户可以改动生成的文件，用于后续推理。
+
+### OLLaMA使用
+
+使用上面的文件，需要安装OLLaMA：
+```shell
+# https://github.com/ollama/ollama
+curl -fsSL https://ollama.com/install.sh | sh
+```
+
+启动OLLaMA:
+
+```shell
+ollama serve
+```
+
+在另一个terminal运行：
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/llama3-8b-instruct-ollama/Modelfile
+```
+
+执行后会打印如下log：
+
+```text
+transferring model data
+unpacking model metadata
+processing tensors
+converting model
+creating new layer sha256:37b0404fb276acb2e5b75f848673566ce7048c60280470d96009772594040706
+creating new layer sha256:2ecd014a372da71016e575822146f05d89dc8864522fdc88461c1e7f1532ba06
+creating new layer sha256:ddc2a243c4ec10db8aed5fbbc5ac82a4f8425cdc4bd3f0c355373a45bc9b6cb0
+creating new layer sha256:fc776bf39fa270fa5e2ef7c6782068acd858826e544fce2df19a7a8f74f3f9df
+writing manifest
+success
+```
+
+之后就可以用命令的名字来推理：
+
+```shell
+ollama run my-custom-model
+```
+
+```shell
+>>> who are you?
+I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. My primary function is to understand and respond to human
+input in a helpful and informative way. I'm a type of AI designed to simulate conversation, answer questions, and even generate text based
+on a given prompt or topic.
+
+I'm not a human, but rather a computer program designed to mimic human-like conversation. I don't have personal experiences, emotions, or
+physical presence, but I'm here to provide information, answer your questions, and engage in conversation to the best of my abilities.
+
+I'm constantly learning and improving my responses based on the interactions I have with users like you, so please bear with me if I make
+any mistakes or don't quite understand what you're asking. I'm here to help and provide assistance, so feel free to ask me anything!
+```
+
+## 需要注意的问题
+
+1. 部分模型在
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/qwen-7b-chat-ollama/Modelfile
+```
+
+的时候会报错:
+
+```shell
+Error: Models based on 'QWenLMHeadModel' are not yet supported
+```
+
+这是因为ollama的转换并不支持所有类型的模型，此时可以自行进行gguf导出并修改Modelfile的FROM字段：
+
+```shell
+# 详细转换步骤可以参考：https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/README.md
+git clone https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp
+# 模型目录可以在`swift export`命令的日志中找到，类似：
+# Using model_dir: /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942-merged
+python convert_hf_to_gguf.py /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942-merged
+```
+
+之后重新执行：
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/qwen-7b-chat-ollama/Modelfile
+```

docs/source/LLM/index.md

@@ -8,14 +8,15 @@
 4. [界面训练与推理](../GetStarted/%E7%95%8C%E9%9D%A2%E8%AE%AD%E7%BB%83%E6%8E%A8%E7%90%86.md)
 5. [LLM评测文档](LLM评测文档.md)
 6. [LLM量化文档](LLM量化文档.md)
-7. [VLLM推理加速与部署](VLLM推理加速与部署.md)
-8. [LmDeploy推理加速与部署](LmDeploy推理加速与部署.md)
-9. [LLM实验文档](LLM实验文档.md)
-10. [DPO训练文档](DPO训练文档.md)
-11. [ORPO最佳实践](ORPO算法最佳实践.md)
-12. [SimPO最佳实践](SimPO算法最佳实践.md)
-13. [人类偏好对齐训练文档](人类偏好对齐训练文档.md)
-14. [Megatron训练文档](Megatron训练文档.md)
+7. [OLLAMA导出文档](OLLAMA导出文档.md)
+8. [VLLM推理加速与部署](VLLM推理加速与部署.md)
+9. [LmDeploy推理加速与部署](LmDeploy推理加速与部署.md)
+10. [LLM实验文档](LLM实验文档.md)
+11. [DPO训练文档](DPO训练文档.md)
+12. [ORPO最佳实践](ORPO算法最佳实践.md)
+13. [SimPO最佳实践](SimPO算法最佳实践.md)
+14. [人类偏好对齐训练文档](人类偏好对齐训练文档.md)
+15. [Megatron训练文档](Megatron训练文档.md)
 
 ### ⭐️最佳实践系列
 

docs/source/index.rst

@@ -26,6 +26,7 @@ Swift DOCUMENTATION
    LLM/人类偏好对齐训练文档.md
    LLM/LLM评测文档.md
    LLM/LLM量化文档.md
+   LLM/OLLAMA导出文档.md
    LLM/VLLM推理加速与部署.md
    LLM/LmDeploy推理加速与部署.md
    LLM/Megatron训练文档.md

docs/source_en/LLM/OLLaMA-Export.md

@@ -0,0 +1,155 @@
+# OLLaMA Export Documentation
+
+SWIFT now supports exporting OLLaMA Model files, integrated into the `swift export` command.
+
+## Contents
+
+- [Environment Setup](#environment-setup)
+- [Export](#export)
+- [Points to Note](#points-to-note)
+
+## Environment Setup
+
+```shell
+# Set pip global mirror (to speed up downloads)
+pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
+# Install ms-swift
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install -e '.[llm]'
+```
+
+No additional modules are needed for OLLaMA export, as SWIFT only exports the ModelFile. Users can handle subsequent operations.
+
+## Export
+
+The OLLaMA export command line is as follows:
+
+```shell
+# model_type
+swift export --model_type llama3-8b-instruct --to_ollama true --ollama_output_dir llama3-8b-instruct-ollama
+# ckpt_dir, note that for lora training, add --merge_lora true
+swift export --ckpt_dir /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942 --to_ollama true --ollama_output_dir qwen-7b-chat-ollama --merge_lora true
+```
+
+After execution, the following log will be printed:
+```shell
+[INFO:swift] Exporting to ollama:
+[INFO:swift] If you have a gguf file, try to pass the file by :--gguf_file /xxx/xxx.gguf, else SWIFT will use the original(merged) model dir
+[INFO:swift] Downloading the model from ModelScope Hub, model_id: LLM-Research/Meta-Llama-3-8B-Instruct
+[WARNING:modelscope] Authentication has expired, please re-login with modelscope login --token "YOUR_SDK_TOKEN" if you need to access private models or datasets.
+[WARNING:modelscope] Using branch: master as version is unstable, use with caution
+[INFO:swift] Loading the model using model_dir: /mnt/workspace/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct
+[INFO:swift] Save Modelfile done, you can start ollama by:
+[INFO:swift] > ollama serve
+[INFO:swift] In another terminal:
+[INFO:swift] > ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/llama3-8b-instruct-ollama/Modelfile
+[INFO:swift] > ollama run my-custom-model
+[INFO:swift] End time of running main: 2024-08-09 17:17:48.768722
+```
+
+Check the Modelfile:
+
+```text
+FROM /mnt/workspace/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct
+TEMPLATE """{{ if .System }}<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ else }}<|begin_of_text|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}<|eot_id|>"""
+PARAMETER stop "<|eot_id|>"
+PARAMETER temperature 0.3
+PARAMETER top_k 20
+PARAMETER top_p 0.7
+PARAMETER repeat_penalty 1.0
+```
+
+Users can modify the generated file for subsequent inference.
+
+### Using OLLaMA
+
+To use the above file, install OLLaMA:
+
+```shell
+# https://github.com/ollama/ollama
+curl -fsSL https://ollama.com/install.sh | sh
+```
+
+Start OLLaMA:
+
+```shell
+ollama serve
+```
+
+In another terminal, run:
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/llama3-8b-instruct-ollama/Modelfile
+```
+
+The following log will be printed after execution:
+
+```text
+transferring model data
+unpacking model metadata
+processing tensors
+converting model
+creating new layer sha256:37b0404fb276acb2e5b75f848673566ce7048c60280470d96009772594040706
+creating new layer sha256:2ecd014a372da71016e575822146f05d89dc8864522fdc88461c1e7f1532ba06
+creating new layer sha256:ddc2a243c4ec10db8aed5fbbc5ac82a4f8425cdc4bd3f0c355373a45bc9b6cb0
+creating new layer sha256:fc776bf39fa270fa5e2ef7c6782068acd858826e544fce2df19a7a8f74f3f9df
+writing manifest
+success
+```
+
+You can then use the command name for inference:
+
+```shell
+ollama run my-custom-model
+```
+
+```shell
+>>> who are you?
+I'm LLaMA, a large language model trained by a team of researchers at Meta AI. My primary function is to understand and respond to human
+input in a helpful and informative way. I'm a type of AI designed to simulate conversation, answer questions, and even generate text based
+on a given prompt or topic.
+
+I'm not a human, but rather a computer program designed to mimic human-like conversation. I don't have personal experiences, emotions, or
+physical presence, but I'm here to provide information, answer your questions, and engage in conversation to the best of my abilities.
+
+I'm constantly learning and improving my responses based on the interactions I have with users like you, so please bear with me if I make
+any mistakes or don't quite understand what you're asking. I'm here to help and provide assistance, so feel free to ask me anything!
+```
+
+## Points to Note
+
+1. Some models may report an error during:
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/qwen-7b-chat-ollama/Modelfile
+```
+
+Error message:
+
+```shell
+Error: Models based on 'QWenLMHeadModel' are not yet supported
+```
+
+This is because the conversion in OLLaMA does not support all types of models. You can perform gguf export yourself and modify the FROM field in the Modelfile:
+
+```shell
+# Detailed conversion steps can be found at: https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/README.md
+git clone https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp
+# The model directory can be found in the `swift export` command log, similar to:
+# Using model_dir: /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942-merged
+python convert_hf_to_gguf.py /mnt/workspace/yzhao/tastelikefeet/swift/output/qwen-7b-chat/v141-20240331-110833/checkpoint-10942-merged
+```
+
+Then re-execute:
+
+```shell
+ollama create my-custom-model -f /mnt/workspace/yzhao/tastelikefeet/swift/qwen-7b-chat-ollama/Modelfile
+```

docs/source_en/LLM/index.md

@@ -8,14 +8,15 @@
 4. [Web-UI Training and Inference](../GetStarted/Web-ui.md)
 5. [LLM Evaluation](LLM-eval.md)
 6. [LLM Quantization](LLM-quantization.md)
-7. [VLLM Inference and Deployment](VLLM-inference-acceleration-and-deployment.md)
-8. [LmDeploy Inference and Deployment](LmDeploy-inference-acceleration-and-deployment.md)
-9. [LLM Experimental](LLM-exp.md)
-10. [DPO Training](DPO.md)
-11. [ORPO Training](ORPO.md)
-12. [SimPO Training](SimPO.md)
-13. [Human Preference Alignment Training Documentation](Human-Preference-Alignment-Training-Documentation.md)
-14. [Megatron-training](Megatron-training.md)
+7. [OLLAMA Export](./OLLaMA-Export.md)
+8. [VLLM Inference and Deployment](VLLM-inference-acceleration-and-deployment.md)
+9. [LmDeploy Inference and Deployment](LmDeploy-inference-acceleration-and-deployment.md)
+10. [LLM Experimental](LLM-exp.md)
+11. [DPO Training](DPO.md)
+12. [ORPO Training](ORPO.md)
+13. [SimPO Training](SimPO.md)
+14. [Human Preference Alignment Training Documentation](Human-Preference-Alignment-Training-Documentation.md)
+15. [Megatron-training](Megatron-training.md)
 
 ### ⭐️Best Practices!
 

docs/source_en/index.rst

@@ -26,6 +26,7 @@ Swift DOCUMENTATION
    LLM/Human-Preference-Alignment-Training-Documentation.md
    LLM/LLM-eval.md
    LLM/LLM-quantization.md
+   LLM/OLLaMA-Export.md
    LLM/VLLM-inference-acceleration-and-deployment.md
    LLM/LmDeploy-inference-acceleration-and-deployment.md
    LLM/Megatron-training.md

swift/llm/export.py

@@ -200,6 +200,7 @@ def llm_export(args: ExportArguments) -> None:
             model_dir = args.ckpt_dir
         else:
             model_dir = args.model_id_or_path
+        logger.info(f'Using model_dir: {model_dir}')
         _, tokenizer = get_model_tokenizer(
             args.model_type, model_id_or_path=model_dir, revision=args.model_revision, load_model=False)
         model_dir = tokenizer.model_dir