commit e1a2e656c06d7d10279acf9d6c83e8d1c4c2dfb6 Author: ModelHub XC Date: Thu May 21 00:04:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: FlagRelease/Qwen3-4B-FlagOS-Ascend Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..d2fc868 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,51 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +merges.txt filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +vocab.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..1634716 --- /dev/null +++ b/README.md @@ -0,0 +1,284 @@ +# Introduction + +**FlagOS** is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the **FlagScale** distributed training/inference framework, **FlagGems** universal operator library, **FlagCX** communication library, and **FlagTree** unified compiler, the **FlagRelease** platform leverages the FlagOS stack to automatically produce and release various combinations of . This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application. + +Based on this, the **Qwen3-4B-FlagOS-Ascend** model is adapted for the Ascend chip using the FlagOS software stack, enabling: + +### Integrated Deployment + +- Deep integration with the open-source [FlagScale framework](https://github.com/FlagOpen/FlagScale) +- Out-of-the-box inference scripts with pre-configured hardware and software parameters +- Released **FlagOS** container image supporting deployment within minutes + +### Consistency Validation + +- Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public. + +# Technical Overview + +## **FlagScale Distributed Training and Inference Framework** + +FlagScale is an end-to-end framework for large models across heterogeneous computing resources, maximizing computational efficiency and ensuring model validity through core technologies. Its key advantages include: + +- **Unified Deployment Interface:** Standardized command-line tools support one-click service deployment across multiple hardware platforms, significantly reducing adaptation costs in heterogeneous environments. +- **Intelligent Parallel Optimization:** Automatically generates optimal distributed parallel strategies based on chip computing characteristics, achieving dynamic load balancing of computation/communication resources. +- **Seamless Operator Switching:** Deep integration with the FlagGems operator library allows high-performance operators to be invoked via environment variables without modifying model code. + +## **FlagGems Universal Large-Model Operator Library** + +FlagGems is a Triton-based, cross-architecture operator library collaboratively developed with industry partners. Its core strengths include: + +- **Full-stack Coverage**: Over 100 operators, with a broader range of operator types than competing libraries. +- **Ecosystem Compatibility**: Supports 7 accelerator backends. Ongoing optimizations have significantly improved performance. +- **High Efficiency**: Employs unique code generation and runtime optimization techniques for faster secondary development and better runtime performance compared to alternatives. + +## **FlagEval Evaluation Framework** + +FlagEval (Libra)** is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features: + +- **Multi-dimensional Evaluation**: Supports 800+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation. +- **Industry-Grade Use Cases**: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation. + +# Evaluation Results + +## Benchmark Result + +| Metrics | Qwen3-4B-H100-CUDA | Qwen3-4B-FlagOS-Ascend | +| --------- | ------------------ | ---------------------- | +| LIVEBENCH | 0.501 | 0.502 | +| AIME | 0.700 | 0.733 | +| GPQA | 0.410 | 0.424 | +| MMLU | 0.669 | 0.670 | +| MUSR | 0.590 | 0.623 | + +# User Guide + +## General Information + +**Environment Setup** + +| System Component | Version Information | +| ------------------------------- | --------------------------------------- | +| Accelerator Card Driver Version | Version: 25.0.rc1 | +| Docker Version | Docker version 20.10.8, build 3967b7d | +| Operating System | PRETTY_NAME="openEuler 22.03 (LTS-SP4)" | +| FlagScale | Version: 0.8.0 | +| FlagGems | Version: 2.2 | + +## Operation Steps + +### Download FlagOS Image + +```python +#Download the image for the A3 chip +docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease-ascend-release-model_qwen3-4b-tree_none-gems_2.2-scale_0.8.0-cx_none-python_3.11.11-torch_npu2.6.0rc1-pcp_cann8.2rc1.alpha002-gpu_ascend001-arc_arm64-driver_25.2.0:2512101714 +#Download the image for the A2 chip +docker pull harbor.baai.ac.cn/flagrelease-public/flagopen-910b-ubuntu24.04.2-py311_ascend_a2:latest +``` + +### Download Open-source Model Weights + +```python +pip install modelscope +modelscope download --model Qwen/Qwen3-4B --local_dir /data/weights/Qwen3-4B/ +``` + +### Start the inference service (A3 chip) + +```python +#Container Startup +docker run -itd --name flagos \ + -u root \ + -w /workspace \ + --privileged=true \ + --shm-size=1000g \ + --net=host \ + -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ + -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ + -v /usr/local/dcmi:/usr/local/dcmi \ + -v /usr/local/sbin:/usr/local/sbin \ + -v /usr/share/zoneinfo/Asia/Shanghai:/etc/localtime \ + -v /etc/ascend_install.info:/etc/ascend_install.info \ + -v /data:/data \ + -v /root/.cache:/root/.cache \ + -e VLLM_USE_V1=1 \ + -e CPU_AFFINITY_CONF=2 \ + -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \ + -e USE_FLAGGEMS=true \ + harbor.baai.ac.cn/flagrelease-public/flagrelease-ascend-release-model_qwen3-4b-tree_none-gems_2.2-scale_0.8.0-cx_none-python_3.11.11-torch_npu2.6.0rc1-pcp_cann8.2rc1.alpha002-gpu_ascend001-arc_arm64-driver_25.2.0:2512101714 bash + +#Enter the container +docker exec -it flagos bash + +#Special configuration is required +source /usr/local/Ascend/ascend-toolkit/set_env.sh && source /usr/local/Ascend/nnal/atb/set_env.sh + +#Use 'pip show flag_scale' to find the installation path of FlagScale. +pip show flag_scale + +# Modify the 4b.yaml file located at flag_scale/examples/qwen3/conf/serve +- serve_id: vllm_model + engine: vllm + engine_args: + model: /data/weights/Qwen3-4B/ # path of weight of Qwen3-4B + served_model_name: Qwen3-4B-ascend-flagos + tensor_parallel_size: 1 + gpu_memory_utilization: 0.8 + host: x.x.x.xxx #Modify the host field in the 4b.yaml configuration file to use the machine's actual IP address. + port: 9010 + block_size: 128 + max_model_len: 35536 + max_num_seqs: 16 + enforce_eager: true + no_enable_prefix_caching: true + no_enable_chunked_prefill: true + +``` + +### Serve(A3 chip) + +```python +flagscale serve qwen3 + +#After the service starts, you will see output similar to the following: +#INFO: Started server process [392] +#INFO: Waiting for application startup. +#INFO: Application startup complete. +``` + +### Start the inference service (A2 chip) + +```python +#Container Startup +docker run -itd --name gems_test \ + -u root \ + -w /workspace \ + --privileged=true \ + --shm-size=1000g \ + --net=host \ + -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ + -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ + -v /usr/local/dcmi:/usr/local/dcmi \ + -v /usr/local/sbin:/usr/local/sbin \ + -v /usr/share/zoneinfo/Asia/Shanghai:/etc/localtime \ + -v /etc/ascend_install.info:/etc/ascend_install.info \ + -v /data:/data \ + -v /root/.cache:/root/.cache \ + -e VLLM_USE_V1=1 \ + -e CPU_AFFINITY_CONF=2 \ + -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \ + -e USE_FLAGGEMS=true \ + -e ASCEND_RT_VISIBLE_DEVICES=7 \ + harbor.baai.ac.cn/flagrelease-public/flagopen-910b-ubuntu24.04.2-py311_ascend_a2:latest bash + +#Enter the container +docker exec -it flagos bash + +#Special configuration is required +source /usr/local/Ascend/ascend-toolkit/set_env.sh +source /usr/local/Ascend/nnal/atb/set_env.sh + +#Use 'pip show flag_scale' to find the installation path of FlagScale. +pip show flag_scale + +# Modify the 4b.yaml file located at flag_scale/examples/qwen3/conf/serve to match the changes made for the A3 chip. + +``` + +### Serve(A2 chip) + +```python +#Start the vLLM service with FlagScale +flagscale serve qwen3 /workspace/FlagScale/examples/qwen3/conf/serve.yaml + +#After the service starts, you will see output similar to the following: +#INFO: Started server process [392] +#INFO: Waiting for application startup. +#INFO: Application startup complete. +``` + +# Service Invocation + +## API-based Invocation Script + +``` +import openai +openai.api_key = "EMPTY" +openai.base_url = "http://:9010/v1/" +model = "Qwen3-4B-ascend-flagos" +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What's the weather like today?"} +] +response = openai.chat.completions.create( + model=model, + messages=messages, + temperature=0.7, + top_p=0.95, + stream=False, +) +for item in response: + print(item) +``` + +## AnythingLLM Integration Guide + +#### 1. Download & Install + +- Visit the official site: https://anythingllm.com/ +- Choose the appropriate version for your OS (Windows/macOS/Linux) +- Follow the installation wizard to complete the setup + +#### 2. Configuration + +- Launch AnythingLLM +- Open settings (bottom left, fourth tab) +- Configure core LLM parameters +- Click "Save Settings" to apply changes + +#### 3. Model Interaction + +- After model loading is complete: + - Click **"New Conversation"** + - Enter your question (e.g., “Explain the basics of quantum computing”) + - Click the send button to get a response + +# Frequently Asked Questions + +### Q1: What should I do if the model fails to load? + +- Check if the model weight path is correct. +- Ensure the Qwen3-4B model files are present in the `/share` directory inside the container. +- Check the container logs: `docker logs flagos`. + +### Q2: API call returns a timeout error. What should I do? + +- Verify that the server IP address is correct. +- Check the firewall settings to ensure port 9010 is open. +- Confirm that the service is running properly: `docker exec flagos ps aux | grep flagscale`. + +### Q3: IP **errors** + +- Modify the 4b.yaml file located at flag_scale/examples/qwen3/conf/serve +![ip](ip.jpg) + +### Q4: The application experiences freezing issues during operation [limited to A2 chips only] + +- *You may attempt to explicitly turn off PD fusion and prefix caching* + + ![p2](p2.jpeg) + +# + +# Contributing + +We warmly welcome global developers to join us: + +1. Submit Issues to report problems +2. Create Pull Requests to contribute code +3. Improve technical documentation +4. 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false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151668": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + } + }, + "additional_special_tokens": [ + "<|im_start|>", + "<|im_end|>", + "<|object_ref_start|>", + "<|object_ref_end|>", + "<|box_start|>", + "<|box_end|>", + "<|quad_start|>", + "<|quad_end|>", + "<|vision_start|>", + "<|vision_end|>", + "<|vision_pad|>", + "<|image_pad|>", + "<|video_pad|>" + ], + "bos_token": null, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in message.content %}\n {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} \ No newline at end of file diff --git a/vocab.json b/vocab.json new file mode 100644 index 0000000..6c49fc6 --- /dev/null +++ b/vocab.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910 +size 2776833