--- title: "Tessera-27B-B-CJK-RL" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/55f45d0c-0ba9-4dad-9289-e73ffa41ea9b" updated: "2026-11-13" --- # Tessera-27B-B-CJK-RL > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs coding-agent loops with a 27B model that thinks in a compressed English and Chinese shorthand, so it spends fewer thinking tokens per turn. For developers who host a large model themselves. Tessera-27B-B-CJK-RL 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 ZelligeAI ## About ### What it is Tessera-27B-B-CJK-RL started from Qwen3.8-27B and was tuned to act as a coding agent. Its private reasoning is a terse mix of English, Chinese words and symbols, so each agent turn costs fewer thinking tokens than the base model needs. Answers and tool calls stay in plain English and code. It extends the earlier Tessera-27B-B-CJK through eight rounds of RL training that used math and code tasks. ### Who it suits Developers who run coding agents on a model they host themselves, and researchers who study short, compressed reasoning. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent builders | Drive a coding-agent loop with a model that thinks briefly each turn | A loop that edits files, runs tests and reports results | | AI research | ML researchers | Compare thinking length and accuracy against the base model | Check per-turn thinking tokens on held-out coding tasks | | Math and code tools | Tool developers | Use the added math and code training for harder problems | Competition-style math questions | You can skip it if you lack hardware for 55.6 GB of bf16 weights, or if you want a model with its own safety tuning, since this fine-tune adds none. ### Where to use it It targets agent loops that you host yourself, and the card ran it through vLLM. - vLLM on a machine that holds the 55.6 GB bf16 weights - The transformers library, which the card used to load the weights - Other builds from the same maker: a PEFT adapter, GGUF files and an NVFP4 and FP8 checkpoint | Use case | Who | What they get back | | --- | --- | --- | | Coding-agent loop | Agent developers | Short thinking per turn and plain tool calls | | Competition math | Researchers | More correct answers than the earlier Tessera model | | Build comparison | ML engineers | A merged model plus adapter and quantized repositories to try | ### How to set it up You need Python and enough storage and memory for a 27.8B parameter model, whose bf16 weights are about 55.6 GB. - Python with pip - About 55.6 GB of disk space for the bf16 weights, or about 13.9 GB at 4-bit as an estimate - vLLM, the version the card used being 0.31 1. Install the Hugging Face download tool. 2. Download the merged weights to your computer. 3. Load them with vLLM, as shown in the next section. ```bash pip install -U huggingface_hub hf download ZelligeAI/Tessera-27B-B-CJK-RL ``` ### How to use it Put it inside an agent loop. Set the reasoning effort to xhigh, set top-k to 20, top-p to 0.95 and temperature to 1.0, and keep earlier thinking out of the message history. The chat template takes low, medium or xhigh as the reasoning effort. The card loaded the weights this way with vLLM. ```python from vllm import LLM, SamplingParams llm = LLM(model="ZelligeAI/Tessera-27B-B-CJK-RL", dtype="bfloat16", max_model_len=65536, limit_mm_per_prompt={"image": 0, "video": 0}) ``` | Tool | What it does | | --- | --- | | vLLM | Serves the merged bf16 weights, as the card ran them | | transformers | Loads the weights, as the card did | | reasoning\_effort | Takes low, medium or xhigh, and the card advises xhigh for agents | ### Example In an agent loop, the model finishes a change and checks its own work. The card shows one such turn, a 45 token think in mixed Chinese and English shorthand. It notes that the test suite matches the baseline with no regressions, then runs lint on the changed files. The base model used 158 tokens at the same state. ### For newcomers and experts A coding agent is a program that lets a model read code, run commands and fix things in a loop. Before each step the model writes private thinking, and this one writes it in a tight shorthand to save tokens. A first small try is to download the weights and load them with the vLLM lines above. For experts, the weights are a beta: a rank-32 LoRA adapter was baked into the bf16 base model. Reinforcement learning on math and code lifted competition scores against the earlier model, and it kept most of the per-turn thinking saving in agent work. The card says the typical turn used 0.67 times the base model's thinking tokens, a little above the earlier model's 0.60. The trade is accuracy against brevity, and the card measures it on 16 held-out coding-agent tasks. Agent loop sends a task state to the model → Model thinks in compressed CJK shorthand → Model answers or calls a tool in plain English → Past thinks are dropped from the history → The loop continues with the next state ### Good to know The numbers below come from the card's own runs with fixed seeds, at xhigh effort and 64K tokens of context. - Beta: The card calls this a beta release, and the quantized GGUF and NVFP4 repositories were not evaluated. - Tested path: The card ran merged weights from a local copy and did not test loading from the Hub. - Safety: Training added no extra safety work on top of what the base model had. Correct attempts out of 126 on competition math | Label | Value | | --- | --- | | Tessera-27B-B-CJK-RL | 108 correct | | Tessera-27B-B-CJK | 97 correct | - License: Apache 2.0. - Cost to run: none named, the source lists no API key, account or paid service, though the weights need a machine that holds about 55.6 GB. - Data it reaches: only the prompts and files your own agent loop gives it, and the model files it downloads from Hugging Face. - Not for: relying on extra safety tuning, since none was added, or on the GGUF and NVFP4 copies, which the card did not evaluate. ## README ````markdown # Tessera-27B-B-CJK-RL Runs coding-agent loops with a 27B model that thinks in a compressed English and Chinese shorthand, so it spends fewer thinking tokens per turn. For developers who host a large model themselves. Made by ZelligeAI 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: Tessera-27B-B-CJK-RL started from Qwen3.8-27B and was tuned to act as a coding agent. Its private reasoning is a terse mix of English, Chinese words and symbols, so each agent turn costs fewer thinking tokens than the base model needs. Answers and tool calls stay in plain English and code. It extends the earlier Tessera-27B-B-CJK through eight rounds of RL training that used math and code tasks. Who it suits: Developers who run coding agents on a model they host themselves, and researchers who study short, compressed reasoning. ```table Industry | Who uses it | How they use it | Example Software | Agent builders | Drive a coding-agent loop with a model that thinks briefly each turn | A loop that edits files, runs tests and reports results AI research | ML researchers | Compare thinking length and accuracy against the base model | Check per-turn thinking tokens on held-out coding tasks Math and code tools | Tool developers | Use the added math and code training for harder problems | Competition-style math questions ``` You can skip it if you lack hardware for 55.6 GB of bf16 weights, or if you want a model with its own safety tuning, since this fine-tune adds none. Where to use it: It targets agent loops that you host yourself, and the card ran it through vLLM. - vLLM on a machine that holds the 55.6 GB bf16 weights - The transformers library, which the card used to load the weights - Other builds from the same maker: a PEFT adapter, GGUF files and an NVFP4 and FP8 checkpoint ```table Use case | Who | What they get back Coding-agent loop | Agent developers | Short thinking per turn and plain tool calls Competition math | Researchers | More correct answers than the earlier Tessera model Build comparison | ML engineers | A merged model plus adapter and quantized repositories to try ``` How to set it up: You need Python and enough storage and memory for a 27.8B parameter model, whose bf16 weights are about 55.6 GB. - Python with pip - About 55.6 GB of disk space for the bf16 weights, or about 13.9 GB at 4-bit as an estimate - vLLM, the version the card used being 0.31 1. Install the Hugging Face download tool. 2. Download the merged weights to your computer. 3. Load them with vLLM, as shown in the next section. ```bash pip install -U huggingface_hub hf download ZelligeAI/Tessera-27B-B-CJK-RL ``` How to use it: Put it inside an agent loop. Set the reasoning effort to xhigh, set top-k to 20, top-p to 0.95 and temperature to 1.0, and keep earlier thinking out of the message history. The chat template takes low, medium or xhigh as the reasoning effort. The card loaded the weights this way with vLLM. ```python from vllm import LLM, SamplingParams llm = LLM(model="ZelligeAI/Tessera-27B-B-CJK-RL", dtype="bfloat16", max_model_len=65536, limit_mm_per_prompt={"image": 0, "video": 0}) ``` ```table Tool | What it does vLLM | Serves the merged bf16 weights, as the card ran them transformers | Loads the weights, as the card did reasoning_effort | Takes low, medium or xhigh, and the card advises xhigh for agents ``` Example: In an agent loop, the model finishes a change and checks its own work. The card shows one such turn, a 45 token think in mixed Chinese and English shorthand. It notes that the test suite matches the baseline with no regressions, then runs lint on the changed files. The base model used 158 tokens at the same state. For newcomers and experts: A coding agent is a program that lets a model read code, run commands and fix things in a loop. Before each step the model writes private thinking, and this one writes it in a tight shorthand to save tokens. A first small try is to download the weights and load them with the vLLM lines above. For experts, the weights are a beta: a rank-32 LoRA adapter was baked into the bf16 base model. Reinforcement learning on math and code lifted competition scores against the earlier model, and it kept most of the per-turn thinking saving in agent work. The card says the typical turn used 0.67 times the base model's thinking tokens, a little above the earlier model's 0.60. The trade is accuracy against brevity, and the card measures it on 16 held-out coding-agent tasks. ```flow Agent loop sends a task state to the model Model thinks in compressed CJK shorthand Model answers or calls a tool in plain English Past thinks are dropped from the history The loop continues with the next state ``` Good to know: The numbers below come from the card's own runs with fixed seeds, at xhigh effort and 64K tokens of context. - Beta: The card calls this a beta release, and the quantized GGUF and NVFP4 repositories were not evaluated. - Tested path: The card ran merged weights from a local copy and did not test loading from the Hub. - Safety: Training added no extra safety work on top of what the base model had. ```chart Title: Correct attempts out of 126 on competition math Tessera-27B-B-CJK-RL: 108 correct Tessera-27B-B-CJK: 97 correct ``` - License: Apache 2.0. - Cost to run: none named, the source lists no API key, account or paid service, though the weights need a machine that holds about 55.6 GB. - Data it reaches: only the prompts and files your own agent loop gives it, and the model files it downloads from Hugging Face. - Not for: relying on extra safety tuning, since none was added, or on the GGUF and NVFP4 copies, which the card did not evaluate. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.8B - Weights: about 55.6 GB in BF16; about 13.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 85 - Likes: 3 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3.8-27B - Quantized versions: 2 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, agentic, coding-agent, efficient-reasoning, reinforcement-learning, tessera, conversational ## Links - Model: https://huggingface.co/ZelligeAI/Tessera-27B-B-CJK-RL - Files: https://huggingface.co/ZelligeAI/Tessera-27B-B-CJK-RL/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ZelligeAI/Tessera-27B-B-CJK-RL ``` 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 [ZelligeAI/Tessera-27B-B-CJK-RL on Hugging Face](https://huggingface.co/ZelligeAI/Tessera-27B-B-CJK-RL) ## 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 | | --- | --- | --- | --- | --- | --- | | Tessera-27B-B-CJK-RL (this page) | 85 | 3 | 27.8B | apache-2.0 | 2026-11-06 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | | [Mage-VL-Cowork](https://mindket.com/p/53460bdf-08aa-4019-bb00-2a650a2d0157) | 4,226 | 222 | 4.7B | apache-2.0 | 2026-10-30 | | [Thinklite-27B](https://mindket.com/p/5493f9ea-b8a7-4bcd-b293-50e8b881da96) | 568 | 51 | 27.8B | apache-2.0 | 2026-11-04 | | [Kvark-Sparkle-1.0-27B](https://mindket.com/p/6451d52d-00e1-4878-8a25-80910fc67ac7) | 315 | 15 | 27.4B | apache-2.0 | 2026-11-05 | | [KVARK Sparkle 1.1 (27B)](https://mindket.com/p/fb8f86c3-be94-494c-9f8e-66d3faba633c) | 59 | 3 | 27.4B | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · Zellige · 2026-11-06](https://news.ycombinator.com/item?id=50399821): Tessera, an open 27B tuned Qwen coding model that thinks in compressed CJK ## 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: 2026-11-06 - First published: 2026-11-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ZelligeAI/Tessera-27B-B-CJK-RL on Hugging Face](https://huggingface.co/ZelligeAI/Tessera-27B-B-CJK-RL) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/55f45d0c-0ba9-4dad-9289-e73ffa41ea9b) - [Its page on Mindket](https://mindket.com/p/55f45d0c-0ba9-4dad-9289-e73ffa41ea9b)