--- title: "Bielik-11B-v3.1-DFlash2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2711a318-550d-40de-b3db-4e250b26a909" updated: "2026-11-13" --- # Bielik-11B-v3.1-DFlash2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Pairs with the Bielik-11B-v3.1-Instruct model as a draft model for speculative decoding in SGLang or vLLM. It is not a standalone language model. Bielik-11B-v3.1-DFlash2 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 speakleash ## About ### What it is Bielik-11B-v3.1-DFlash2 is a small draft model for speculative decoding. It is meant to run next to Bielik-11B-v3.1-Instruct, the larger target model, inside an inference server that supports DFlash. On its own it does not work as a language model. ### Who it suits It suits teams that already serve the Bielik-11B-v3.1-Instruct model, mainly for Polish, and want to try speculative decoding with a matching draft. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Language technology | Polish NLP teams | Pair the draft with the Instruct model on their own server | Serving a Polish chat model through vLLM | | Inference engineering | Platform engineers | Try DFlash speculative decoding in SGLang or vLLM | Comparing the two servers' launch setups | | Research | Applied ML groups | Study draft models trained on the target's own data | Reading how the draft was trained on the same dataset | You can skip it if you do not run the Instruct model, because the draft does nothing alone. ### Where to use it - SGLang, version 0.5.19 or newer - vLLM, version 0.28.0 or newer - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Polish chat serving | Platform teams | A speculative decoding setup for the Instruct model | | Server comparison | Inference engineers | One draft model tried in both SGLang and vLLM | | Draft model study | Researchers | A draft trained on the same data as its target | ### How to set it up - Python, to run the Hugging Face download tool - The huggingface\_hub package - SGLang 0.5.19 or newer, or vLLM 0.28.0 or newer - The larger Instruct model that the draft pairs with 1. Install the Hugging Face hub package. 2. Download the draft model files to your computer. 3. Start SGLang or vLLM with the Instruct model as the main model and this one as the draft. ```sh pip install -U huggingface_hub hf download speakleash/Bielik-11B-v3.1-DFlash2 ``` The card's SGLang launch looks like this. ```sh export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server \ --model-path speakleash/Bielik-11B-v3.1-Instruct \ --speculative-algorithm DFLASH \ --speculative-draft-model-path speakleash/Bielik-11B-v3.1-DFlash2 \ --speculative-num-draft-tokens 16 ``` The card's vLLM launch looks like this. ```sh vllm serve speakleash/Bielik-11B-v3.1-Instruct \ --speculative-config '{"method": "dflash", "model": "speakleash/Bielik-11B-v3.1-DFlash2", "num_speculative_tokens": 15}' \ --attention-backend flash_attn \ --max-num-batched-tokens 32768 ``` ### How to use it You start the server with the Instruct model as the main model and name this model as the draft. After that you use the server the way you already use the Instruct model. | Tool | What it does | | --- | --- | | python -m sglang.launch\_server | Starts SGLang with the target model and the draft | | --speculative-num-draft-tokens | Sets how many tokens the draft proposes, 16 in the card's example | | vllm serve | Starts vLLM with the target model | | --speculative-config | Names the dflash method, the draft model and 15 draft tokens | ### Example You launch vLLM with the Instruct model and this draft in the speculative config, then chat with the model in Polish as before. The replies come from the Instruct model, and the draft only proposes tokens for it to check. ### For newcomers and experts A draft model is a small helper that guesses the next few words so a larger model can check them together. This one never writes answers alone. To start, download it and launch the vLLM example above. If you know the field, this is a DFlash2 draft with 1.2B parameters, trained on the target's own dataset with a custom setup built on ALLaMo. The cost is that it ties you to one target model and to a server with DFlash support. You send a request to the server → The draft proposes several tokens → The Instruct model checks them together → The accepted tokens form the reply ### Good to know Draft tokens in the card's two setups | Label | Value | | --- | --- | | SGLang setup | 16 draft tokens | | vLLM setup | 15 draft tokens | - Not standalone: the card says it must be paired with its target model - Languages: 32 European languages, tuned for Polish - Size: about 2.5 GB in BF16, or about 617 MB at 4-bit as an estimate - License: Apache 2.0 - Cost to run: none named, but you also need the Instruct model and a supported server - Data it reaches: the prompts you send to your own server, and the files you download from Hugging Face - Not for: standalone text generation, since it is only a draft ## README ````markdown # Bielik-11B-v3.1-DFlash2 Pairs with the Bielik-11B-v3.1-Instruct model as a draft model for speculative decoding in SGLang or vLLM. It is not a standalone language model. Made by speakleash 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: Bielik-11B-v3.1-DFlash2 is a small draft model for speculative decoding. It is meant to run next to Bielik-11B-v3.1-Instruct, the larger target model, inside an inference server that supports DFlash. On its own it does not work as a language model. Who it suits: It suits teams that already serve the Bielik-11B-v3.1-Instruct model, mainly for Polish, and want to try speculative decoding with a matching draft. ```table Industry | Who uses it | How they use it | Example Language technology | Polish NLP teams | Pair the draft with the Instruct model on their own server | Serving a Polish chat model through vLLM Inference engineering | Platform engineers | Try DFlash speculative decoding in SGLang or vLLM | Comparing the two servers' launch setups Research | Applied ML groups | Study draft models trained on the target's own data | Reading how the draft was trained on the same dataset ``` You can skip it if you do not run the Instruct model, because the draft does nothing alone. Where to use it: - SGLang, version 0.5.19 or newer - vLLM, version 0.28.0 or newer - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Polish chat serving | Platform teams | A speculative decoding setup for the Instruct model Server comparison | Inference engineers | One draft model tried in both SGLang and vLLM Draft model study | Researchers | A draft trained on the same data as its target ``` How to set it up: - Python, to run the Hugging Face download tool - The huggingface_hub package - SGLang 0.5.19 or newer, or vLLM 0.28.0 or newer - The larger Instruct model that the draft pairs with 1. Install the Hugging Face hub package. 2. Download the draft model files to your computer. 3. Start SGLang or vLLM with the Instruct model as the main model and this one as the draft. ```sh pip install -U huggingface_hub hf download speakleash/Bielik-11B-v3.1-DFlash2 ``` The card's SGLang launch looks like this. ```sh export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server \ --model-path speakleash/Bielik-11B-v3.1-Instruct \ --speculative-algorithm DFLASH \ --speculative-draft-model-path speakleash/Bielik-11B-v3.1-DFlash2 \ --speculative-num-draft-tokens 16 ``` The card's vLLM launch looks like this. ```sh vllm serve speakleash/Bielik-11B-v3.1-Instruct \ --speculative-config '{"method": "dflash", "model": "speakleash/Bielik-11B-v3.1-DFlash2", "num_speculative_tokens": 15}' \ --attention-backend flash_attn \ --max-num-batched-tokens 32768 ``` How to use it: You start the server with the Instruct model as the main model and name this model as the draft. After that you use the server the way you already use the Instruct model. ```table Tool | What it does python -m sglang.launch_server | Starts SGLang with the target model and the draft --speculative-num-draft-tokens | Sets how many tokens the draft proposes, 16 in the card's example vllm serve | Starts vLLM with the target model --speculative-config | Names the dflash method, the draft model and 15 draft tokens ``` Example: You launch vLLM with the Instruct model and this draft in the speculative config, then chat with the model in Polish as before. The replies come from the Instruct model, and the draft only proposes tokens for it to check. For newcomers and experts: A draft model is a small helper that guesses the next few words so a larger model can check them together. This one never writes answers alone. To start, download it and launch the vLLM example above. If you know the field, this is a DFlash2 draft with 1.2B parameters, trained on the target's own dataset with a custom setup built on ALLaMo. The cost is that it ties you to one target model and to a server with DFlash support. ```flow You send a request to the server The draft proposes several tokens The Instruct model checks them together The accepted tokens form the reply ``` Good to know: ```chart Title: Draft tokens in the card's two setups SGLang setup: 16 draft tokens vLLM setup: 15 draft tokens ``` - Not standalone: the card says it must be paired with its target model - Languages: 32 European languages, tuned for Polish - Size: about 2.5 GB in BF16, or about 617 MB at 4-bit as an estimate - License: Apache 2.0 - Cost to run: none named, but you also need the Instruct model and a supported server - Data it reaches: the prompts you send to your own server, and the files you download from Hugging Face - Not for: standalone text generation, since it is only a draft ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.2B - Weights: about 2.5 GB in BF16; about 617 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 44 - Likes: 3 - License: apache-2.0 - Languages: pl, en - Quantized versions: 2 - Tags: transformers, safetensors, qwen3, dflash, speculative-decoding, block-diffusion, draft-model, efficiency, bielik, diffusion-language-model, text-generation, multilingual ## Links - Model: https://huggingface.co/speakleash/Bielik-11B-v3.1-DFlash2 - Files: https://huggingface.co/speakleash/Bielik-11B-v3.1-DFlash2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download speakleash/Bielik-11B-v3.1-DFlash2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [speakleash/Bielik-11B-v3.1-DFlash2 on Hugging Face](https://huggingface.co/speakleash/Bielik-11B-v3.1-DFlash2) ## 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 | | --- | --- | --- | --- | --- | --- | | Bielik-11B-v3.1-DFlash2 (this page) | 44 | 3 | 1.2B | apache-2.0 | 2026-11-11 | | [Bielik-11B-v3.1-DFlash](https://mindket.com/p/2f296c12-93ae-4573-91ac-4ee308bc1fdf) | 36 | 3 | 1.2B | apache-2.0 | 2026-11-11 | | [Qwen3.8-27B-DFlash2 by incoai](https://mindket.com/p/916f5a21-666c-4741-94ab-62684c958ac0) | 429,580 | 301 | 1.9B | apache-2.0 | 2026-09-17 | | [Qwen3.8-Flash-Next-DFlash2](https://mindket.com/p/395037c6-711b-471c-8552-e9f88ed25ab4) | 58,226 | 84 | 603M | apache-2.0 | 2026-10-19 | | [Qwen3.8-27B-DFlash2](https://mindket.com/p/92c81d30-6c87-4d00-8e96-3e1241439f5d) | 46,660 | 335 | 1.9B | apache-2.0 | 2026-08-19 | | [Flint-DFlash2](https://mindket.com/p/097e9887-da16-4838-9d9a-ac148ae4398b) | 1,651 | 9 | 403M | apache-2.0 | 2026-10-26 | 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: 2026-11-11 - First published: 2026-10-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open speakleash/Bielik-11B-v3.1-DFlash2 on Hugging Face](https://huggingface.co/speakleash/Bielik-11B-v3.1-DFlash2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2711a318-550d-40de-b3db-4e250b26a909) - [Its page on Mindket](https://mindket.com/p/2711a318-550d-40de-b3db-4e250b26a909)