--- title: "dale-9b (GGUF)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dc84987f-1c48-4ce0-9213-3c452c5fb2b4" updated: "2026-11-13" --- # dale-9b (GGUF) > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns a Spanish or English request into one Linux bash command, returned as a small JSON object. For terminal users and developers who want a local command helper that runs on CPU or GPU. dale-9b (GGUF) is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by manuelgscom ## About ### What it is dale-9b is a fine-tuned Qwen3.5-9B that reads a request written in Spanish or English and answers with one bash command for Linux. The answer comes back as a small JSON object. This copy is in GGUF format, so it can run on a CPU or GPU through llama.cpp tools. ### Who it suits It suits people who live in a Linux terminal and cannot always recall the exact command, and developers who want a local command generator inside their own tool. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Describe a shell task in plain words and get a command | Asking for a way to find large files | | IT operations | Sysadmins | Draft Linux commands offline before reviewing them | Turning a note in Spanish into one command | | Education | Linux learners | See which command matches a plain request | Comparing the output with a manual page | | Tooling | App builders | Serve the model with llama-server behind their own app | Adding a command helper to a notes tool | You can skip it if you do not use Linux, or if you want a model that writes anything other than a single command. ### Where to use it The model works in the dale command line tool or in any app that talks to llama-server. - The dale command line tool, which runs it on a GPU through Vulkan - Any other app that can call llama-server, running on CPU or GPU - Linux, since the commands it writes are for Linux | Use case | Who | What they get back | | --- | --- | --- | | Plain request to command | Terminal users | One bash command in a JSON answer | | Spanish or English asks | Bilingual teams | The same kind of answer in either language | | Local helper inside an app | App builders | A model served on your own machine | ### How to set it up You need Python for the download tool and enough disk and memory for a model of about 5.6 GB. The dale tool uses this model on a dedicated GPU with at least 8 GiB of free video memory, and you choose it at the first-run question. - Python, for the Hugging Face download tool - About 5.6 GB of disk for the Q4\_K\_M file - A GPU with 8 GiB of free video memory, if you use it through dale 1. Install the Hugging Face download tool. 2. Download the files of this repository. 3. Run the model with the dale tool or with llama-server. ```bash pip install -U huggingface_hub hf download manuelgscom/dale-9b-GGUF ``` ### How to use it Write your request in Spanish or English and let the model answer with its JSON object. The model was trained with a fixed prompt and with reasoning switched off, so keep that format. Outside dale there is no risk check, so read each command before you run it. | Tool | What it does | | --- | --- | | dale | Command line tool that runs the model offline, explains the command, rates the risk and runs it only after you confirm | | llama-server | Serves the GGUF file to any other application | | hf download | Fetches the model files to your computer | ### Example You ask in Spanish for a command that lists the files in a folder, sorted by size. The model returns a small JSON object holding a single bash command. Inside dale you also get an explanation and a risk rating, and nothing runs until you confirm. ### For newcomers and experts If you are new, treat it as a translator from plain words to a shell command. Try a harmless request first, and read the command before running it. It scored 89.2 percent on the author's public 471-case test through llama-server, against 62.8 percent for the untuned base. Through dale on the GPU it scored 89.6 percent. Plain request in Spanish or English → Fixed prompt, reasoning off → dale-9b writes a JSON answer → One bash command comes back → You review it, then run it ### Good to know The dale family has four sizes of the same model. Only the 4B and 9B ones use the GPU in the dale tool. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but the dale tool wants about 5.4 GiB of video memory for this model - Data it reaches: runs offline on your machine, and the commands it suggests can touch whatever your shell can reach once you run them - Not for: running commands unchecked outside dale, or tasks beyond writing one Linux command Test v2 accuracy by dale size | Label | Value | | --- | --- | | dale-0.8b | 79.4% | | dale-2b | 81.7% | | dale-4b | 87.0% | | dale-9b | 89.2% | ## README ````markdown # dale-9b (GGUF) Turns a Spanish or English request into one Linux bash command, returned as a small JSON object. For terminal users and developers who want a local command helper that runs on CPU or GPU. Made by manuelgscom on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: dale-9b is a fine-tuned Qwen3.5-9B that reads a request written in Spanish or English and answers with one bash command for Linux. The answer comes back as a small JSON object. This copy is in GGUF format, so it can run on a CPU or GPU through llama.cpp tools. Who it suits: It suits people who live in a Linux terminal and cannot always recall the exact command, and developers who want a local command generator inside their own tool. ```table Industry | Who uses it | How they use it | Example Software | Developers | Describe a shell task in plain words and get a command | Asking for a way to find large files IT operations | Sysadmins | Draft Linux commands offline before reviewing them | Turning a note in Spanish into one command Education | Linux learners | See which command matches a plain request | Comparing the output with a manual page Tooling | App builders | Serve the model with llama-server behind their own app | Adding a command helper to a notes tool ``` You can skip it if you do not use Linux, or if you want a model that writes anything other than a single command. Where to use it: The model works in the dale command line tool or in any app that talks to llama-server. - The dale command line tool, which runs it on a GPU through Vulkan - Any other app that can call llama-server, running on CPU or GPU - Linux, since the commands it writes are for Linux ```table Use case | Who | What they get back Plain request to command | Terminal users | One bash command in a JSON answer Spanish or English asks | Bilingual teams | The same kind of answer in either language Local helper inside an app | App builders | A model served on your own machine ``` How to set it up: You need Python for the download tool and enough disk and memory for a model of about 5.6 GB. The dale tool uses this model on a dedicated GPU with at least 8 GiB of free video memory, and you choose it at the first-run question. - Python, for the Hugging Face download tool - About 5.6 GB of disk for the Q4_K_M file - A GPU with 8 GiB of free video memory, if you use it through dale 1. Install the Hugging Face download tool. 2. Download the files of this repository. 3. Run the model with the dale tool or with llama-server. ```bash pip install -U huggingface_hub hf download manuelgscom/dale-9b-GGUF ``` How to use it: Write your request in Spanish or English and let the model answer with its JSON object. The model was trained with a fixed prompt and with reasoning switched off, so keep that format. Outside dale there is no risk check, so read each command before you run it. ```table Tool | What it does dale | Command line tool that runs the model offline, explains the command, rates the risk and runs it only after you confirm llama-server | Serves the GGUF file to any other application hf download | Fetches the model files to your computer ``` Example: You ask in Spanish for a command that lists the files in a folder, sorted by size. The model returns a small JSON object holding a single bash command. Inside dale you also get an explanation and a risk rating, and nothing runs until you confirm. For newcomers and experts: If you are new, treat it as a translator from plain words to a shell command. Try a harmless request first, and read the command before running it. It scored 89.2 percent on the author's public 471-case test through llama-server, against 62.8 percent for the untuned base. Through dale on the GPU it scored 89.6 percent. ```flow Plain request in Spanish or English Fixed prompt, reasoning off dale-9b writes a JSON answer One bash command comes back You review it, then run it ``` Good to know: The dale family has four sizes of the same model. Only the 4B and 9B ones use the GPU in the dale tool. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but the dale tool wants about 5.4 GiB of video memory for this model - Data it reaches: runs offline on your machine, and the commands it suggests can touch whatever your shell can reach once you run them - Not for: running commands unchecked outside dale, or tasks beyond writing one Linux command ```chart Title: Test v2 accuracy by dale size dale-0.8b: 79.4% dale-2b: 81.7% dale-4b: 87.0% dale-9b: 89.2% ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 9B - Weights: about 4.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 292 - Likes: 3 - License: apache-2.0 - Languages: es, en - Based on: Qwen/Qwen3.5-9B - Tags: gguf, bash, shell, nl2bash, llama.cpp, vulkan, text-generation, model-index, imatrix, conversational ## Links - Model: https://huggingface.co/manuelgscom/dale-9b-GGUF - Files: https://huggingface.co/manuelgscom/dale-9b-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download manuelgscom/dale-9b-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [manuelgscom/dale-9b-GGUF on Hugging Face](https://huggingface.co/manuelgscom/dale-9b-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | dale-9b (GGUF) (this page) | 292 | 3 | 9B | apache-2.0 | 2026-10-29 | | [dale-0.8b-GGUF](https://mindket.com/p/6fbea688-a5e2-4613-9a8f-f004c4cb29ab) | 2,163 | 44 | 752M | apache-2.0 | 2026-11-08 | | [askmdb-9b-GGUF](https://mindket.com/p/867806d8-1d95-4280-b990-c2b1211598b6) | 351 | 5 | 9B | apache-2.0 | 2026-11-08 | | [Qwen 3.6 35B-A3B REAP-48 v2 GGUF](https://mindket.com/p/303f4298-cc6d-452d-a68a-d4be1ae2a110) | 2,427 | 34 | 19.2B | apache-2.0 | 2026-08-25 | | [Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF](https://mindket.com/p/2b5092f0-78de-42d0-ae72-417592fa56e9) | 17,179 | 296 | 34.7B | apache-2.0 | 2026-04-19 | | [Sharp-Spark-X2.5-4B-GGUF](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c) | 875,346 | 164 | 4.1B | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-29 - First published: 2026-10-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open manuelgscom/dale-9b-GGUF on Hugging Face](https://huggingface.co/manuelgscom/dale-9b-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dc84987f-1c48-4ce0-9213-3c452c5fb2b4) - [Its page on Mindket](https://mindket.com/p/dc84987f-1c48-4ce0-9213-3c452c5fb2b4)