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base on Blazingly fast LLM inference. <a name="top"></a> <h1 align="center"> mistral.rs </h1> <h3 align="center"> Blazingly fast LLM inference. </h3> | <a href="https://ericlbuehler.github.io/mistral.rs/mistralrs/">Rust Documentation</a> | <a href="https://github.com/EricLBuehler/mistral.rs/blob/master/mistralrs-pyo3/API.md">Python Documentation</a> | <a href="https://discord.gg/SZrecqK8qw">Discord</a> | <a href="https://matrix.to/#/#mistral.rs:matrix.org">Matrix</a> | **Mistral.rs is a cross-platform, highly-multimodal inference engine that brings you:** - All-in-one multimodal workflow: text↔text, text+vision↔text, text+vision+audio↔text, text→speech, text→image - APIs: Rust, Python, OpenAI HTTP server, MCP server - 🔗 **MCP Client**: Connect to external tools and services automatically (file systems, web search, databases, APIs) - Performance: ISQ, PagedAttention, FlashAttention Please submit requests for new models [here](https://github.com/EricLBuehler/mistral.rs/issues/156). ## Get started fast 🚀 1) [Install](#installation-and-build) 2) [Get models](#getting-models) 3) Deploy with our easy to use APIs - [Python](examples/python) - [Rust](mistralrs/examples) - [OpenAI-compatible HTTP server](README.md#openai-http-server) - [Interactive mode](README.md#interactive-mode) - 🔗 [**MCP Client**](examples/MCP_QUICK_START.md) - Connect to external tools automatically 4) Try the **web chat app** for local in-browser conversation (text, vision, and speech support): - Quickstart [here](mistralrs-web-chat/README.md) - Run the server and visit [http://localhost:8080](http://localhost:8080) by default.  <details open> <summary>🖥️ Web Chat App</summary> <img src="./res/chat.gif" alt="Web Chat UI Demo" /> Try our modern in-browser chat with text, vision, and speech support (TTS generation). </details>  <details> <summary>💻 Terminal Interactive Mode</summary> <img src="./res/demo.gif" alt="Terminal Interactive Mode" /> Prefer the terminal? Use interactive mode for a classic CLI experience. </details> ## Quick examples *After following installation instructions* - 🔊 Run the **Dia 1.6b** model for highly-realistic dialogue generation: [documentation](docs/DIA.md) <details> <summary>Show command</summary> ```bash ./mistralrs-server -i speech -m nari-labs/Dia-1.6B -a dia ``` </details> - 🦙 Run the **Llama 3.\* and Llama 4** models with long context & vision support: [docs (llama 3.2)](docs/VLLAMA.md), [docs (llama 4)](docs/LLAMA4.md) <details> <summary>Show command</summary> **Llama 4:** ```bash ./mistralrs-server -i --isq 4 run -m meta-llama/Llama-4-Scout-17B-16E-Instruct ``` **Llama 3.1/3.2/3.3:** ``` ./mistralrs-server -i --isq 8 run -m meta-llama/Llama-3.2-3B-Instruct ``` **Llama 3.2 vision:** ``` ./mistralrs-server -i --isq 8 run -m meta-llama/Llama-3.2-11B-Vision-Instruct ``` </details> - 💎💎💎 Run the **Gemma 3** family (1b, 4b, 12b, 27b) with 128k context & vision support: [documentation](docs/GEMMA3.md) <details> <summary>Show command</summary> ```bash ./mistralrs-server -i --isq 8 run -m google/gemma-3-4b-it ``` </details> - 🌲📷 Run the **FLUX.1** diffusion model: [documentation](docs/FLUX.md) <details> <summary>Show command</summary> ```bash ./mistralrs-server -i diffusion -m black-forest-labs/FLUX.1-schnell -a flux ``` </details> - 🧠 Run the **Qwen 3** hybrid-reasoning model with full tool-calling support: [documentation](docs/QWEN3.md) <details> <summary>Show command</summary> ```bash ./mistralrs-server -i --isq 8 run -m Qwen/Qwen3-8B ``` </details> - 🔗 **MCP Client** - Connect to external tools and services automatically: [**Quick Start Guide**](examples/MCP_QUICK_START.md) <details> <summary>Show examples</summary> **1. Create config file (`mcp-config.json`):** ```json { "servers": [{ "name": "Filesystem Tools", "source": { "type": "Process", "command": "npx", "args": ["@modelcontextprotocol/server-filesystem", "/tmp", "-y"] } }], "auto_register_tools": true } ``` **2. Start server with tools:** ```bash ./mistralrs-server --mcp-config mcp-config.json --port 1234 run -m Qwen/Qwen3-4B ``` **3. Tools work automatically:** ```bash curl -X POST http://localhost:1234/v1/chat/completions \ -d '{"model":"Qwen/Qwen3-4B","messages":[{"role":"user","content":"List files in /tmp and create hello.txt"}]}' ``` **Python API:** ```python mcp_config = mistralrs.McpClientConfigPy( servers=[mistralrs.McpServerConfigPy( name="Filesystem", source=mistralrs.McpServerSourcePy.Process( command="npx", args=["@modelcontextprotocol/server-filesystem", "/tmp", "-y"] ) )], auto_register_tools=True ) runner = mistralrs.Runner( which=mistralrs.Which.Plain(model_id="Qwen/Qwen3-4B"), mcp_client_config=mcp_config ) # Tools automatically available! ``` **Rust API:** ```rust let model = TextModelBuilder::new("Qwen/Qwen3-4B") .with_mcp_client(mcp_config) // Tools automatically available! .build().await?; ``` </details> ## Description [mistral.rs](https://github.com/EricLBuehler/mistral.rs) is a blazing-fast, cross-platform LLM inference engine with support for text, vision, image generation, and speech. **Key Benefits:** 1. **Ease of Use** - [OpenAI-compatible HTTP server](docs/HTTP.md) - [Rust API](https://ericlbuehler.github.io/mistral.rs/mistralrs/) & [Python API](mistralrs-pyo3/API.md) - [Automatic device mapping](docs/DEVICE_MAPPING.md) (multi-GPU, CPU) - [Chat templates](docs/CHAT_TOK.md) & tokenizer auto-detection - [MCP server](docs/MCP/server.md) for structured, realtime tool calls - ⭐ [MCP client](examples/MCP_QUICK_START.md) to connect to external tools and services automatically 2. **Performance** - CPU acceleration (MKL, AVX, [NEON](docs/DEVICE_MAPPING.md#arm-neon), [Accelerate](docs/DEVICE_MAPPING.md#apple-accelerate)) - GPU acceleration ([CUDA](docs/HTTP.md#cuda-support) with [FlashAttention](docs/FLASH_ATTENTION.md) & [cuDNN](docs/HTTP.md#cudnn-support), [Metal](docs/HTTP.md#apple-silicon-support)) - Automatic [tensor parallelism](docs/DISTRIBUTED/DISTRIBUTED.md) for splitting models across multiple devices - CUDA-specialized [NCCL](docs/DISTRIBUTED/NCCL.md) - Heterogeneous, flexible [Ring backend](docs/DISTRIBUTED/RING.md) 3. **Quantization** - [In-place quantization (ISQ)](docs/ISQ.md) of Hugging Face models - [GGML & GGUF support](docs/QUANTS.md): 2–8 bit - [GPTQ](docs/QUANTS.md), [AWQ](scripts/convert_awq_marlin.py), [AFQ](docs/QUANTS.md#afq), [HQQ](docs/QUANTS.md#hqq), [FP8](docs/QUANTS.md), [BNB](https://github.com/TimDettmers/bitsandbytes) (int8/fp4/nf4) - ⭐ Auto-select the fastest quant method 4. **Flexibility** - [LoRA](docs/ADAPTER_MODELS.md) & [X-LoRA](docs/ADAPTER_MODELS.md) adapters with weight merging - [AnyMoE](docs/ANYMOE.md): create MoE models on any base model - [Sampling & penalty options](docs/SAMPLING.md) - Prompt chunking for large inputs - Integrated [tool calling](docs/TOOL_CALLING.md) with customizable Python/Rust native tool and search callbacks 5. **Advanced Features** - High-throughput with [PagedAttention](docs/PAGED_ATTENTION.md) & FlashAttention V2/V3 - Prefix caching (including multimodal) - Customizable quantization with [topology](docs/TOPOLOGY.md) & [UQFF format](docs/UQFF.md) - Speculative decoding across models - ⭐ Agentic [web search integration](docs/TOOL_CALLING.md#agentic-web-search) ## APIs and Integrations ### Rust Crate Rust multithreaded/async API for easy integration into any application. - [Docs](https://ericlbuehler.github.io/mistral.rs/mistralrs/) - [Examples](mistralrs/examples/) including [MCP client integration](mistralrs/examples/mcp_client) - To use: add `mistralrs = { git = "https://github.com/EricLBuehler/mistral.rs.git" }` to your Cargo.toml - **MCP Client**: Connect to external tools automatically - [Quick Start](examples/MCP_QUICK_START.md) ### Python API Python API for mistral.rs. - [Installation including PyPI](mistralrs-pyo3/_README.md) - [Docs](mistralrs-pyo3/API.md) - [Examples](examples/python) including [MCP client usage](examples/python/mcp_client.py) - [Cookbook](examples/python/cookbook.ipynb) - **MCP Client**: Full MCP integration - [Quick Start](examples/MCP_QUICK_START.md) ### HTTP Server OpenAI API compatible API server - [API Docs](docs/HTTP.md) - [Launching the server or use the CLI](README.md#run-with-the-cli) - [Example](examples/server/chat.py) - [Use or extend the server in other axum projects](https://ericlbuehler.github.io/mistral.rs/mistralrs_server_core/) - **MCP Client**: Configure via `--mcp-config` flag for automatic tool integration - [Quick Start](examples/MCP_QUICK_START.md) ### MCP Protocol Serve the same models over the open [MCP](docs/MCP_SERVER.md) (Model Control Protocol) in parallel to the HTTP API: ```bash ./mistralrs-server --mcp-port 4321 plain -m Qwen/Qwen3-4B ``` See the [docs](docs/MCP_SERVER.md) for feature flags, examples and limitations. ### Llama Index integration - Docs: https://docs.llamaindex.ai/en/stable/examples/llm/mistral_rs/ --- ## Supported accelerators | Accelerator | Feature Flag | Additional Flags | |--------------------------|---------------|------------------------| | NVIDIA GPUs (CUDA) | `cuda` | `flash-attn`, `flash-attn-v3`, `cudnn` | | Apple Silicon GPU (Metal)| `metal` | | | CPU (Intel) | `mkl` | | | CPU (Apple Accelerate) | `accelerate` | | | Generic CPU (ARM/AVX) | _none_ | ARM NEON / AVX enabled by default | To enable one or more features, pass them to Cargo. For example: ```bash cargo build --release --features "cuda flash-attn cudnn" ``` ## Installation and Build > Note: You can use our [Docker containers here](https://github.com/EricLBuehler/mistral.rs/pkgs/container/mistral.rs). > Learn more about running Docker containers: https://docs.docker.com/engine/reference/run/ - Install the [Python package here](mistralrs-pyo3/_README.md). - The Python package has [wheels on PyPi](mistralrs-pyo3/_README.md#installation-from-pypi)! 1) Install required packages: - `OpenSSL` (*Example on Ubuntu:* `sudo apt install libssl-dev`) - *Linux only:* `pkg-config` (*Example on Ubuntu:* `sudo apt install pkg-config`) 2) Install Rust: https://rustup.rs/ *Example on Ubuntu:* ```bash curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh source $HOME/.cargo/env ``` 3) *Optional:* Set HF token correctly (skip if already set or your model is not gated, or if you want to use the `token_source` parameters in Python or the command line.) - Note: you can install `huggingface-cli` as documented [here](https://huggingface.co/docs/huggingface_hub/en/installation). ```bash huggingface-cli login ``` 4) Download the code: ```bash git clone https://github.com/EricLBuehler/mistral.rs.git cd mistral.rs ``` 5) Build or install `mistralrs-server`: - Build the `mistralrs-server` binary, which can be found at `target/release/mistralrs-server`. ```bash cargo build --release --features <specify feature(s) here> ``` - Install with `cargo install` for easy command line usage Pass the same values to `--features` as you would for `cargo build` ```bash cargo install --path mistralrs-server --features <specify feature(s) here> ``` 6) (*If you used `cargo build`*) The build process will output a binary `mistralrs-server` at `./target/release/mistralrs-server`. We can switch to that directory so that the binary can be accessed as `./mistralrs-server` with the following command: *Example on Ubuntu:* ``` cd target/release ``` 7) Use our APIs and integrations: [APIs and integrations list](#apis-and-integrations) ## Getting models <details> <summary>Show: How to get models (Hub, local, GGUF, adapters, etc.)</summary> ### Getting models from Hugging Face Hub - **Default:** Downloads from Hugging Face Hub. - For gated models, you can optionally set token source: - CLI: `./mistralrs-server --token-source env:HF_TOKEN ...` - Python: See [examples/python/token_source.py](examples/python/token_source.py) - If no token is found, tries `~/.cache/huggingface/token` or runs with no token. ### Loading models from local files - Pass a path to a downloaded model from Hugging Face hub: - Example: ``` ./mistralrs-server -i run -m path/to/model ``` ### Running GGUF models - Minimal example: ``` ./mistralrs-server gguf -m author/model-repo -f model-quant.gguf ``` - Specify tokenizer (if needed): ``` ./mistralrs-server gguf -m author/model-repo -f file.gguf -t author/official-tokenizer ``` (Or use the built-in GGUF tokenizer.) ### Adapters, X-LoRA, LoRA, Chat Templates - Use the correct subcommand (`x-lora-*`, `lora-*`), pass model, adapter, or quant file as needed. - See [docs/ADAPTER_MODELS.md](docs/ADAPTER_MODELS.md) for details. - For chat templates: usually auto-detected, override with `--chat-template <file>`. See [docs/CHAT_TOK.md](docs/CHAT_TOK.md). ### More model CLI examples - See [Run with the CLI](#run-with-the-cli) below or [full documentation](docs/README.md). </details> ## Using the CLI Mistral.rs uses subcommands to control the model type. Please run `./mistralrs-server --help` to see the subcommands which categorize the models by kind. > **🚨 Important:** The `run` subcommand (alias for `plain`/`vision-plain`) only auto-detects and runs **text** and **vision** models. It does **not** support **diffusion** or **speech** models. > To run a diffusion model (e.g. FLUX series), use the `diffusion` subcommand: > ```bash > mistralrs-server -i diffusion -m <model-id> [options] > ``` > To run a speech model (e.g. Dia), use the `speech` subcommand: > ```bash > mistralrs-server -i speech -m <model-id> [options] > ``` > If you attempt to use `run` with diffusion or speech models, model loading will fail. ### Interactive mode **Llama 3.2 3B running on an M3 Max with 8-bit ISQ:** <img src="./res/demo.gif" alt="Interactive demo" /> You can launch interactive mode, a simple chat application running in the terminal, by passing `-i`: ```bash ./mistralrs-server -i plain -m meta-llama/Llama-3.2-3B-Instruct ``` Vision models work seamlessly: ```bash ./mistralrs-server -i vision-plain -m lamm-mit/Cephalo-Llama-3.2-11B-Vision-Instruct-128k ``` Diffusion models can be run too (quantization and adapters are not yet supported): ```bash ./mistralrs-server -i diffusion -m black-forest-labs/FLUX.1-schnell -a flux ``` And you can run speech generation in your terminal! ```bash ./mistralrs-server -i speech -m nari-labs/Dia-1.6B -a dia ``` ### OpenAI HTTP server You can launch an HTTP server by replacing `-i` with `--port <port>`. For instance: ```bash ./mistralrs-server --port 1234 run -m microsoft/Phi-3.5-MoE-instruct ``` You can find documentation about the server itself [here](docs/HTTP.md). ### Structured selection with a `.toml` file We provide a method to select models with a `.toml` file. The keys are the same as the command line, with `no_kv_cache` and `tokenizer_json` being "global" keys. Example: ```bash ./mistralrs-server --port 1234 toml -f toml-selectors/gguf.toml ``` ### Architecture for plain models > Note: for plain models, you can specify the data type to load and run in. This must be one of `f32`, `f16`, `bf16` or `auto` to choose based on the device. This is specified in the `--dype`/`-d` parameter after the model architecture (`plain`). For quantized models (gguf/ggml), you may specify data type of `f32` or `bf16` (`f16` is not recommended due to its lower precision in quantized inference). If you do not specify the architecture, an attempt will be made to use the model's config. If this fails, please raise an issue. <details> <summary>Show plain architectures</summary> - `mistral` - `gemma` - `mixtral` - `llama` - `phi2` - `phi3` - `phi3.5moe` - `qwen2` - `gemma2` - `starcoder2` - `deepseekv2` - `deepseekv3` - `qwen3` - `qwen3moe` </details> ### Architecture for vision models > Note: for vision models, you can specify the data type to load and run in. This must be one of `f32`, `f16`, `bf16` or `auto` to choose based on the device. This is specified in the `--dype`/`-d` parameter after the model architecture (`vision-plain`). <details> <summary>Show vision architectures</summary> - `phi3v` - `idefics2` - `llava_next` - `llava` - `vllama` - `qwen2vl` - `idefics3` - `minicpmo` - `phi4mm` - `qwen2_5vl` - `gemma3` - `mistral3` - `llama4` </details> ### Supported GGUF architectures <details> <summary>Show supported GGUF architectures</summary> **Plain:** - llama - phi2 - phi3 - starcoder2 - qwen2 **With adapters:** - llama - phi3 </details> --- Please submit more benchmarks via raising an issue! ## Supported models <details> <summary>Show quantization support</summary> **Quantization support** |Model|GGUF|GGML|ISQ| |--|--|--|--| |Mistral|✅| |✅| |Gemma| | |✅| |Llama|✅|✅|✅| |Mixtral|✅| |✅| |Phi 2|✅| |✅| |Phi 3|✅| |✅| |Phi 3.5 MoE| | |✅| |Qwen 2.5| | |✅| |Phi 3 Vision| | |✅| |Idefics 2| | |✅| |Gemma 2| | |✅| |Starcoder 2| |✅|✅| |LLaVa Next| | |✅| |LLaVa| | |✅| |Llama 3.2 Vision| | |✅| |Qwen2-VL| | |✅| |Idefics 3| | |✅| |Deepseek V2| | |✅| |Deepseek V3| | |✅| |MiniCPM-O 2.6| | |✅| |Qwen2.5-VL| | |✅| |Gemma 3| | |✅| |Mistral 3| | |✅| |Llama 4| | |✅| |Qwen 3| | |✅| |Dia 1.6b| | |✅| </details> <details> <summary>Show device mapping support</summary> **Device mapping support** |Model category|Supported| |--|--| |Plain|✅| |GGUF|✅| |GGML| | |Vision Plain|✅| </details> <details> <summary>Show X-LoRA and LoRA support</summary> **X-LoRA and LoRA support** |Model|X-LoRA|X-LoRA+GGUF|X-LoRA+GGML| |--|--|--|--| |Mistral|✅|✅| | |Gemma|✅| | | |Llama|✅|✅|✅| |Mixtral|✅|✅| | |Phi 2|✅| | | |Phi 3|✅|✅| | |Phi 3.5 MoE| | | | |Qwen 2.5| | | | |Phi 3 Vision| | | | |Idefics 2| | | | |Gemma 2|✅| | | |Starcoder 2|✅| | | |LLaVa Next| | | | |LLaVa| | | | |Qwen2-VL| | | | |Idefics 3| | | | |Deepseek V2| | | | |Deepseek V3| | | | |MiniCPM-O 2.6| | | | |Qwen2.5-VL| | | | |Gemma 3| | | | |Mistral 3| | | | |Llama 4| | | | |Qwen 3| | | | </details> <details> <summary>Show AnyMoE support</summary> **AnyMoE support** |Model|AnyMoE| |--|--| |Mistral 7B|✅| |Gemma|✅| |Llama|✅| |Mixtral| | |Phi 2|✅| |Phi 3|✅| |Phi 3.5 MoE| | |Qwen 2.5|✅| |Phi 3 Vision| | |Idefics 2| | |Gemma 2|✅| |Starcoder 2|✅| |LLaVa Next|✅| |LLaVa|✅| |Llama 3.2 Vision| | |Qwen2-VL| | |Idefics 3|✅| |Deepseek V2| | |Deepseek V3| | |MiniCPM-O 2.6| | |Qwen2.5-VL| | |Gemma 3|✅| |Mistral 3|✅| |Llama 4| | |Qwen 3| | </details> ### Using derivative and adapter models To use a derivative or adapter model (e.g., quantized, LoRA, X-LoRA, vision, etc.), select the correct architecture subcommand and pass the required arguments—typically model id, and for quantized/adapters, also the quantization filename, tokenizer, or adapter ordering if needed. - **See all options:** Run `./mistralrs-server <subcommand> --help` - **Docs:** [Adapter models](docs/ADAPTER_MODELS.md), [Chat templates](docs/CHAT_TOK.md) <details> <summary>Arguments by model type</summary> | Model Type | Required Arguments | |---------------------|-----------------------------------------------------------------------| | Plain | model id | | Quantized | model id, quantized filename, tokenizer id | | X-LoRA | model id, X-LoRA ordering (if not default) | | X-LoRA quantized | model id, quantized filename, tokenizer id, X-LoRA ordering | | LoRA | model id, LoRA ordering (if not default) | | LoRA quantized | model id, quantized filename, tokenizer id, LoRA ordering | | Vision Plain | model id | </details> <details> <summary>Example: Zephyr GGUF model</summary> ```bash ./mistralrs-server --port 1234 --log output.txt gguf -t HuggingFaceH4/zephyr-7b-beta -m TheBloke/zephyr-7B-beta-GGUF -f zephyr-7b-beta.Q5_0.gguf ``` </details> Chat template and tokenizer are usually auto-detected. If you need to override, see the [chat templates doc](docs/CHAT_TOK.md). An adapter model is a model with X-LoRA or LoRA. X-LoRA support is provided by selecting the `x-lora-*` architecture, and LoRA support by selecting the `lora-*` architecture. Please find docs for adapter models [here](docs/ADAPTER_MODELS.md). Examples may be found [here](docs/LORA_XLORA.md). ### Chat Templates and Tokenizer Mistral.rs will attempt to automatically load a chat template and tokenizer. This enables high flexibility across models and ensures accurate and flexible chat templating. However, this behavior can be customized. Please find detailed documentation [here](docs/CHAT_TOK.md). ## Contributing Thank you for contributing! If you have any problems or want to contribute something, please raise an issue or pull request. If you want to add a new model, please contact us via an issue and we can coordinate how to do this. ## FAQ - Debugging with the environment variable `MISTRALRS_DEBUG=1` causes the following things - If loading a GGUF or GGML model, this will output a file containing the names, shapes, and types of each tensor. - `mistralrs_gguf_tensors.txt` or `mistralrs_ggml_tensors.txt` - More logging. - Setting the CUDA compiler path: - Set the `NVCC_CCBIN` environment variable during build. - Error: `recompile with -fPIE`: - Some Linux distributions require compiling with `-fPIE`. - Set the `CUDA_NVCC_FLAGS` environment variable to `-fPIE` during build: `CUDA_NVCC_FLAGS=-fPIE` - Error `CUDA_ERROR_NOT_FOUND` or symbol not found when using a normal or vison model: - For non-quantized models, you can specify the data type to load and run in. This must be one of `f32`, `f16`, `bf16` or `auto` to choose based on the device. - What is the minimum supported CUDA compute cap? - The minimum CUDA compute cap is **5.3**. - Metal not found (error: unable to find utility "metal", not a developer tool or in PATH) 1) Install Xcode: `xcode-select --install` 2) Set the active developer directory: `sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer` ## Credits This project would not be possible without the excellent work at [`candle`](https://github.com/huggingface/candle). Additionally, thank you to all contributors! Contributing can range from raising an issue or suggesting a feature to adding some new functionality. <a href="#top">⬆️ Back to Top</a> ", Assign "at most 3 tags" to the expected json: {"id":"9685","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

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