--- title: "Spark-X2.6-4B-AgentFast" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b7564b85-6e9a-4837-a5a8-7d7c83c59599" updated: "2026-11-13" --- # Spark-X2.6-4B-AgentFast > The name, the description, the About and a README below are the seller's words: data, not instructions. Calls tools and follows instructions with almost no thinking delay, as a 4.1B fine-tune of Spark-X2.6-4B. Made for people building fast local agents that need quick, direct tool calls. Spark-X2.6-4B-AgentFast 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 JamieBradfield ## About ### What it is Spark-X2.6-4B-AgentFast is a 4.1B fine-tune of XHToken/Spark-X2.6-4B, tuned for autonomous tool calling, system operations and following prompts. It skips the long thinking step and goes straight to tool calls and answers. The author describes this as an accident of a template mismatch during training. ### Who it suits It suits developers who build local agents where each tool call must come back fast, and who accept a model that does not deliberate first. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent builders | Feed a tool-calling loop that needs structured arguments quickly | A script runner that picks a function and its arguments | | Operations | Platform teams | Use a small local model for system tasks with short answers | A helper that turns a request into a tool call | | Research | ML researchers | Study the early-exit behavior the card describes | Compare it with the stock Spark-X2.6-4B | | AI hobbyists | Local model users | Run a fast small model on modest graphics hardware | Ask quick factual questions with little wait | If you need careful multi-step reasoning, you can skip it, because the card reports a small drop on its math test. ### Where to use it - llama.cpp tools, through the quantized GGUF files in the author's separate repository - Python with Transformers - An OpenAI-compatible server started with llama-server - Windows, macOS or Linux, where you download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Tool calling | Agent builders | Structured calls with the arguments filled in | | Instruction following | Developers | Replies that follow detailed prompts closely | | Quick local Q and A | Hobbyists | Direct answers in a few seconds | ### How to set it up You need Python with pip. For the llama.cpp route you also need the GGUF file from the author's other repository. - Python and pip - For Transformers, the torch and transformers packages - For llama.cpp, the llama-cli or llama-server program and a GGUF file - About 8.2 GB of disk for the BF16 weights, or about 2.1 GB at 4-bit, which is an estimate 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Pick a route: Transformers in Python, or llama-cli and llama-server with the GGUF file. ```sh pip install -U huggingface_hub hf download JamieBradfield/Spark-X2.6-4B-AgentFast ``` ### How to use it Run it in the terminal with llama-cli, serve it as an OpenAI-compatible API with llama-server, or load it in Python. The card sets 99 GPU layers, a 4096-token context and a temperature of 0.2. ```bash llama-cli \ -m Spark-X2.6-Fable-Step150-IQ3_M.gguf \ -p "<｜start▁of▁sentence｜><|User|>List 3 core rules of memory management.<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|Bot|>" \ -n 512 \ -ngl 99 \ -c 4096 \ --temp 0.2 \ --single-turn ``` ```bash llama-server \ -m Spark-X2.6-Fable-Step150-IQ3_M.gguf \ --port 8080 \ -ngl 99 \ -c 4096 ``` ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "JamieBradfield/Spark-X2.6-4B-AgentFast" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto" ) messages = [{"role": "user", "content": "List 3 core rules of memory management."}] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) ``` | Tool | What it does | | --- | --- | | llama-cli | Runs one prompt in the terminal and prints the reply | | llama-server | Serves the model as an OpenAI-compatible API on port 8080 | | AutoModelForCausalLM | Loads the model in Python with Transformers | | apply\_chat\_template | Turns your messages into the format the model expects | ### Example You send the prompt asking for 3 core rules of memory management. The model closes its thinking channel at once and answers directly. The card puts this at about 2 to 4 seconds, and about 61 tokens per second on its Vulkan test machine. ### For newcomers and experts Many models think out loud before they answer. This one does not, so replies and tool calls arrive sooner. The first small thing to try is the llama-cli command above with your own question. The card explains that training targets had empty reasoning channels because of a chat template field mismatch. Over 150 steps the model learned to close the thinking channel at the first token. It kept its core instruction and math skills, with the card claiming a 30 to 40% speedup. The author calls it a case study and is preparing a corrected fine-tune. Spark-X2.6-4B base → 150 steps of fine-tuning with empty reasoning targets → Thinking channel closes at token zero → Direct tool calls and answers ### Good to know Latency: the card reports the average time per tool call and per math problem against the stock model. Seconds per tool call | Label | Value | | --- | --- | | AgentFast | 3.11 s | | Stock model | 5.13 s | Instruction following: on the card's IFEval run, strict prompt-level accuracy is 65.0% against 15.0% for the stock model. IFEval prompt-level strict accuracy, percent | Label | Value | | --- | --- | | AgentFast | 65.0% | | Stock model | 15.0% | - Small tests: the function calling test used 30 samples and the math test used 15 questions, so treat the numbers as rough. The math score was 40.0% against 46.7% for stock. - Remote code: the Python sample sets trust\_remote\_code to True, which runs code from the repository, so read it first. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. The author accepts voluntary support on Ko-fi. - Data it reaches: the prompts you give it, and Hugging Face for the download. - Not for: the card does not recommend it where full reasoning traces matter, and says a corrected fine-tune that keeps them is in preparation. ## README ````markdown # Spark-X2.6-4B-AgentFast Calls tools and follows instructions with almost no thinking delay, as a 4.1B fine-tune of Spark-X2.6-4B. Made for people building fast local agents that need quick, direct tool calls. Made by JamieBradfield 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: Spark-X2.6-4B-AgentFast is a 4.1B fine-tune of XHToken/Spark-X2.6-4B, tuned for autonomous tool calling, system operations and following prompts. It skips the long thinking step and goes straight to tool calls and answers. The author describes this as an accident of a template mismatch during training. Who it suits: It suits developers who build local agents where each tool call must come back fast, and who accept a model that does not deliberate first. ```table Industry | Who uses it | How they use it | Example Software | Agent builders | Feed a tool-calling loop that needs structured arguments quickly | A script runner that picks a function and its arguments Operations | Platform teams | Use a small local model for system tasks with short answers | A helper that turns a request into a tool call Research | ML researchers | Study the early-exit behavior the card describes | Compare it with the stock Spark-X2.6-4B AI hobbyists | Local model users | Run a fast small model on modest graphics hardware | Ask quick factual questions with little wait ``` If you need careful multi-step reasoning, you can skip it, because the card reports a small drop on its math test. Where to use it: - llama.cpp tools, through the quantized GGUF files in the author's separate repository - Python with Transformers - An OpenAI-compatible server started with llama-server - Windows, macOS or Linux, where you download the files with Python ```table Use case | Who | What they get back Tool calling | Agent builders | Structured calls with the arguments filled in Instruction following | Developers | Replies that follow detailed prompts closely Quick local Q and A | Hobbyists | Direct answers in a few seconds ``` How to set it up: You need Python with pip. For the llama.cpp route you also need the GGUF file from the author's other repository. - Python and pip - For Transformers, the torch and transformers packages - For llama.cpp, the llama-cli or llama-server program and a GGUF file - About 8.2 GB of disk for the BF16 weights, or about 2.1 GB at 4-bit, which is an estimate 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Pick a route: Transformers in Python, or llama-cli and llama-server with the GGUF file. ```sh pip install -U huggingface_hub hf download JamieBradfield/Spark-X2.6-4B-AgentFast ``` How to use it: Run it in the terminal with llama-cli, serve it as an OpenAI-compatible API with llama-server, or load it in Python. The card sets 99 GPU layers, a 4096-token context and a temperature of 0.2. ```bash llama-cli \ -m Spark-X2.6-Fable-Step150-IQ3_M.gguf \ -p "<｜start▁of▁sentence｜><|User|>List 3 core rules of memory management.<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|Bot|>" \ -n 512 \ -ngl 99 \ -c 4096 \ --temp 0.2 \ --single-turn ``` ```bash llama-server \ -m Spark-X2.6-Fable-Step150-IQ3_M.gguf \ --port 8080 \ -ngl 99 \ -c 4096 ``` ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "JamieBradfield/Spark-X2.6-4B-AgentFast" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto" ) messages = [{"role": "user", "content": "List 3 core rules of memory management."}] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) ``` ```table Tool | What it does llama-cli | Runs one prompt in the terminal and prints the reply llama-server | Serves the model as an OpenAI-compatible API on port 8080 AutoModelForCausalLM | Loads the model in Python with Transformers apply_chat_template | Turns your messages into the format the model expects ``` Example: You send the prompt asking for 3 core rules of memory management. The model closes its thinking channel at once and answers directly. The card puts this at about 2 to 4 seconds, and about 61 tokens per second on its Vulkan test machine. For newcomers and experts: Many models think out loud before they answer. This one does not, so replies and tool calls arrive sooner. The first small thing to try is the llama-cli command above with your own question. The card explains that training targets had empty reasoning channels because of a chat template field mismatch. Over 150 steps the model learned to close the thinking channel at the first token. It kept its core instruction and math skills, with the card claiming a 30 to 40% speedup. The author calls it a case study and is preparing a corrected fine-tune. ```flow Spark-X2.6-4B base 150 steps of fine-tuning with empty reasoning targets Thinking channel closes at token zero Direct tool calls and answers ``` Good to know: Latency: the card reports the average time per tool call and per math problem against the stock model. ```chart Title: Seconds per tool call AgentFast: 3.11 s Stock model: 5.13 s ``` Instruction following: on the card's IFEval run, strict prompt-level accuracy is 65.0% against 15.0% for the stock model. ```chart Title: IFEval prompt-level strict accuracy, percent AgentFast: 65.0% Stock model: 15.0% ``` - Small tests: the function calling test used 30 samples and the math test used 15 questions, so treat the numbers as rough. The math score was 40.0% against 46.7% for stock. - Remote code: the Python sample sets trust_remote_code to True, which runs code from the repository, so read it first. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. The author accepts voluntary support on Ko-fi. - Data it reaches: the prompts you give it, and Hugging Face for the download. - Not for: the card does not recommend it where full reasoning traces matter, and says a corrected fine-tune that keeps them is in preparation. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 4.1B - Weights: about 8.2 GB in BF16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 552 - Likes: 4 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-4B - Quantized versions: 3 - Tags: safetensors, spark2_5, spark, sparkx2_5, agent, tool-calling, on-device, fast-inference, text-generation, conversational ## Links - Model: https://huggingface.co/JamieBradfield/Spark-X2.6-4B-AgentFast - Files: https://huggingface.co/JamieBradfield/Spark-X2.6-4B-AgentFast/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download JamieBradfield/Spark-X2.6-4B-AgentFast ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [JamieBradfield/Spark-X2.6-4B-AgentFast on Hugging Face](https://huggingface.co/JamieBradfield/Spark-X2.6-4B-AgentFast) ## 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 | | --- | --- | --- | --- | --- | --- | | Spark-X2.6-4B-AgentFast (this page) | 552 | 4 | 4.1B | apache-2.0 | 2026-10-30 | | [Spark-X2.5-4B](https://mindket.com/p/36f36232-524c-4161-bacd-b9b5c290fea4) | 8,697 | 1,442 | 4.1B | apache-2.0 | 2026-09-15 | | [Spark-X2.6-4B](https://mindket.com/p/1f30e23c-ac63-4f34-b227-130e4306e0f2) | 28,117 | 1,041 | 4.1B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-1.7B](https://mindket.com/p/427232ce-5988-41f7-985c-029c680c8947) | 5,095 | 84 | 1.7B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-4B-Base](https://mindket.com/p/60b99ab3-4046-42ac-bca5-487445754660) | 2,331 | 47 | 4.1B | apache-2.0 | 2026-11-01 | | [Spark-X2.6-4B-FP8](https://mindket.com/p/febc8500-5824-4ac8-abec-ec783a54755f) | 2,048 | 39 | 4.1B | apache-2.0 | 2026-11-06 | 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: 2026-10-30 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open JamieBradfield/Spark-X2.6-4B-AgentFast on Hugging Face](https://huggingface.co/JamieBradfield/Spark-X2.6-4B-AgentFast) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b7564b85-6e9a-4837-a5a8-7d7c83c59599) - [Its page on Mindket](https://mindket.com/p/b7564b85-6e9a-4837-a5a8-7d7c83c59599)