--- title: "Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87" updated: "2026-11-13" --- # Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled > The name, the description, the About and a README below are the seller's words: data, not instructions. Thinks in explicit think blocks before it answers, tuned on about 8k reasoning traces from Claude Opus 4.7. A 35B mixture-of-experts Qwen3.6 variant for hard math, science, code and planning. Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled is a kit in the Research 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 lordx64 ## About ### What it is A community fine-tune of Qwen3.6-35B-A3B that learned to reason in the style of Claude Opus 4.7. The author trained it on about 7,800 reasoning conversations produced by that model, so it writes out its thinking inside think tags before the final answer. It is not an Anthropic release. ### Who it suits People who run their own models for hard reasoning, such as graduate-level science, competition math, code walk-throughs, logic puzzles and agent planning. It fits those who want open weights rather than a proprietary API. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Math and science students | Work through hard problems with visible reasoning | A step-by-step solution to a competition problem | | Software | Developers | Ask for code reasoning with an explicit walk-through | Tracing why a function fails | | Research | AI researchers | Study reasoning distillation into an open model | Comparing it with its base model | | Agent building | Agent developers | Plan multi-step tasks where thinking helps accuracy | A planner that reasons before choosing a tool | You can skip it if you need fast, short chat replies, since the thinking can run to many thousands of tokens. ### Where to use it - Python with the transformers library - vLLM, which the author recommends for serving - LM Studio and llama.cpp, through the separate GGUF copies - A GPU with 80 GB, such as an A100 or H100, for full bf16 | Use case | Who | What they get back | | --- | --- | --- | | Hard reasoning questions | Students and researchers | A long think block, then a final answer | | Serving an open reasoning model | Developers | An endpoint that streams reasoning and answers | | Local chat | Enthusiasts | A quantized copy in LM Studio | ### How to set it up - Python on Windows, macOS or Linux - The huggingface\_hub package, and transformers or vLLM to run it - About 71.9 GB for the BF16 weights, or about 18 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model with transformers, or serve it with vLLM. ```sh pip install -U huggingface_hub hf download lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled ``` ### How to use it Send a chat message and let the model finish its thinking before it answers. Lower max\_new\_tokens to shorten the output. If your app shows only the answer, remove the think blocks afterwards. ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ) messages = [{"role": "user", "content": "How many positive integers less than 1000 have digits that sum to 20?"}] inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(inputs, max_new_tokens=32768, do_sample=False) print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True)) ``` To serve it, the card recommends vLLM. ```sh vllm serve lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled \ --dtype bfloat16 --max-model-len 65536 --gpu-memory-utilization 0.9 ``` | Tool | What it does | | --- | --- | | transformers | Loads the model and runs generation | | vllm serve | Serves the model with continuous batching | | max\_new\_tokens | Caps how long the thinking and answer can run | | GGUF copies | Quantized files for LM Studio and llama.cpp | ### Example You ask how many three-digit numbers have digits that add up to 15. The model writes a long think block that works through the cases, then gives a short final answer. You can check the steps it showed before trusting the number. ### For newcomers and experts If you are new, try a quantized GGUF copy in LM Studio first, since it needs far less memory. Ask a short math question and watch the thinking appear before the answer. If you know the field: the base has 256 experts with 8 routed and 1 shared, and about 3B parameters active per token. The LoRA fine-tune targets only the attention projections and trains the loss on assistant tokens, think blocks included. Claude Opus 4.7 reasoning traces → SFT on Qwen3.6-35B-A3B → Think block then answer → 64k context ### Good to know The author lists three GGUF sizes in separate repositories, from a small default pick to a near-lossless one. A LoRA adapter is also published on its own. GGUF file sizes in GB | Label | Value | | --- | --- | | IQ4\_XS | 18.9 GB | | Q5\_K\_M | 25 GB | | Q8\_0 | 35 GB | - Context: 64k tokens, with thinking that often runs 5k to 30k tokens on hard problems - Training data: about 7,800 conversations from Claude Opus 4.7, reformatted for fine-tuning - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, nothing else named - Not for: latency-sensitive short chat, since the thinking budget can be large ## README ````markdown # Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled Thinks in explicit think blocks before it answers, tuned on about 8k reasoning traces from Claude Opus 4.7. A 35B mixture-of-experts Qwen3.6 variant for hard math, science, code and planning. Made by lordx64 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: A community fine-tune of Qwen3.6-35B-A3B that learned to reason in the style of Claude Opus 4.7. The author trained it on about 7,800 reasoning conversations produced by that model, so it writes out its thinking inside think tags before the final answer. It is not an Anthropic release. Who it suits: People who run their own models for hard reasoning, such as graduate-level science, competition math, code walk-throughs, logic puzzles and agent planning. It fits those who want open weights rather than a proprietary API. ```table Industry | Who uses it | How they use it | Example Education | Math and science students | Work through hard problems with visible reasoning | A step-by-step solution to a competition problem Software | Developers | Ask for code reasoning with an explicit walk-through | Tracing why a function fails Research | AI researchers | Study reasoning distillation into an open model | Comparing it with its base model Agent building | Agent developers | Plan multi-step tasks where thinking helps accuracy | A planner that reasons before choosing a tool ``` You can skip it if you need fast, short chat replies, since the thinking can run to many thousands of tokens. Where to use it: - Python with the transformers library - vLLM, which the author recommends for serving - LM Studio and llama.cpp, through the separate GGUF copies - A GPU with 80 GB, such as an A100 or H100, for full bf16 ```table Use case | Who | What they get back Hard reasoning questions | Students and researchers | A long think block, then a final answer Serving an open reasoning model | Developers | An endpoint that streams reasoning and answers Local chat | Enthusiasts | A quantized copy in LM Studio ``` How to set it up: - Python on Windows, macOS or Linux - The huggingface_hub package, and transformers or vLLM to run it - About 71.9 GB for the BF16 weights, or about 18 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model with transformers, or serve it with vLLM. ```sh pip install -U huggingface_hub hf download lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled ``` How to use it: Send a chat message and let the model finish its thinking before it answers. Lower max_new_tokens to shorten the output. If your app shows only the answer, remove the think blocks afterwards. ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ) messages = [{"role": "user", "content": "How many positive integers less than 1000 have digits that sum to 20?"}] inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(inputs, max_new_tokens=32768, do_sample=False) print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True)) ``` To serve it, the card recommends vLLM. ```sh vllm serve lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled \ --dtype bfloat16 --max-model-len 65536 --gpu-memory-utilization 0.9 ``` ```table Tool | What it does transformers | Loads the model and runs generation vllm serve | Serves the model with continuous batching max_new_tokens | Caps how long the thinking and answer can run GGUF copies | Quantized files for LM Studio and llama.cpp ``` Example: You ask how many three-digit numbers have digits that add up to 15. The model writes a long think block that works through the cases, then gives a short final answer. You can check the steps it showed before trusting the number. For newcomers and experts: If you are new, try a quantized GGUF copy in LM Studio first, since it needs far less memory. Ask a short math question and watch the thinking appear before the answer. If you know the field: the base has 256 experts with 8 routed and 1 shared, and about 3B parameters active per token. The LoRA fine-tune targets only the attention projections and trains the loss on assistant tokens, think blocks included. ```flow Claude Opus 4.7 reasoning traces SFT on Qwen3.6-35B-A3B Think block then answer 64k context ``` Good to know: The author lists three GGUF sizes in separate repositories, from a small default pick to a near-lossless one. A LoRA adapter is also published on its own. ```chart Title: GGUF file sizes in GB IQ4_XS: 18.9 GB Q5_K_M: 25 GB Q8_0: 35 GB ``` - Context: 64k tokens, with thinking that often runs 5k to 30k tokens on hard problems - Training data: about 7,800 conversations from Claude Opus 4.7, reformatted for fine-tuning - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, nothing else named - Not for: latency-sensitive short chat, since the thinking budget can be large ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 36B - Weights: about 71.9 GB in BF16; about 18 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 674 - Likes: 206 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3.6-35B-A3B - Quantized versions: 47 - Fine-tunes: 5 - Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, text-generation, reasoning, distillation, chain-of-thought, qwen, qwen3.6, mixture-of-experts, lora ## Links - Model: https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled - Files: https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled ``` 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 [lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled on Hugging Face](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled (this page) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | | [Qwen3.5-9B Brainwaves DeepThought STEM OneJev1.6](https://mindket.com/p/25badbfb-bc11-4ff8-b0d9-e0ee6b514287) | 57 | 5 | 9.4B | apache-2.0 | 2026-11-07 | | [Sage-A1.5](https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd) | 22,224 | 404 | 36B | apache-2.0 | 2026-10-22 | | [Axion-1.0-35B](https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1) | 1,535 | 76 | 35.1B | apache-2.0 | 2026-11-04 | | [ConeML 1.2B Experimental](https://mindket.com/p/abfa4d0f-0c8b-4a38-9901-2fa4a37bc1d8) | 440 | 2 | 1.2B | cc-by-nc-4.0 | 2026-11-10 | | [Qwen-Image-Bench](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | 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 · pascal-maker · 2026-06-13](https://news.ycombinator.com/item?id=48516940): Statement on US government directive to suspend access to Fable 5 and Mythos 5: Honestly speaking, this is an old trick by the government. They see AI (LLM) as a nuclear weapon which, in my opinion, outside of hallucinations, malicious prompt injections, and AI psychosis, is not an actual threat. The reason I think Anthropic is doing this—preventing distillation and making… ## 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: 2026-04-23 - First published: 2026-04-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled on Hugging Face](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a8bee0d0-7f10-412b-a709-a6267eb87a87) - [Its page on Mindket](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87)