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Cataloged from DavidAU/Qwen3.5-9B-Claude-4.6-Opus-Deckard-V4.2-Uncensored-Heretic-Thinking-GGUF
All models by DavidAU →Fine tune via Unsloth of Qwen 3.5 9B dense model using Claude-4.6 Opus Dataset and DECKARD (5 datasets) on local hardware using two different training sessions and a unique form of merging at training level.
Every attempt was made to ensure the training was "mild" and did not negatively affect the model's already incrediblely strong benchmarks.
Training improves reasoning, and output generation and exceeds the root model on all benchmarks too.
In this specific case, training (with Claude dataset) was used to ADJUST, not REPLACE, the Qwen reasoning - to "TRIM" it.
TECH NOTES:
This is also a HERETIC model, trained post "Heretic'ing" -> this model does what you want, no questions asked.
Fully uncensored.
GGUF(s):
Example generation(s) at the bottom of the page.
Vision (images) tested -> working with new training.
VERSION 4.2 ??!?!
The name is from testing the model:
(full gen at the bottom of the page)
BENCHMARKS:
DE-CENSORING:
KLD of less than 1 is excellent, zero is perfect.
NOTES:
IMPORTANT:
Using an "uncensored" (refusals removed) model VS trained "uncensored" model
Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.
In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.
Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want) to get it generate the content correctly as the "expected" content level too.
Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.
Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic, cursing or explicit levels.
Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.
Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
OTHER OPTIONS:
Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
Qwen3.5 features the following enhancement:
Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.
For more details, please refer to our blog post Qwen3.5.
* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.
* MMLU-ProX: we report the averaged accuracy on 29 languages.
* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
* Empty cells (--) indicate scores not yet available or not applicable.
* MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.
* BabyVision: scores reported as "with CI / without CI".
* TIR-Bench and V*: scores reported as "with CI / without CI".
* Empty cells (--) indicate scores not yet available or not applicable.
[!Important] Qwen3.5 models operate in thinking mode by default, generating thinking content signified by
<think>\n...</think>\n\nbefore producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.
For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API.
Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models.
[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.
[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.
SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:
See its documentation for more details.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
Tool Use: To support tool use, you can use the following command.
Multi-Token Prediction (MTP): The following command is recommended for MTP:
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:
See its documentation for more details.
For detailed Qwen3.5 usage guide, see the vLLM Qwen3.5 recipe.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
Tool Call: To support tool use, you can use the following command.
Multi-Token Prediction (MTP): The following command is recommended for MTP:
Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:
KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.5 with KTransformers, see the KTransformers Deployment Guide.
Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment.
The latest transformers is required for Qwen3.5:
See its documentation for more details. Please also make sure torchvision and pillow are installed.
Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:
The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.
Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:
[!Tip] We recommend using the following set of sampling parameters for generation
- Thinking mode for general tasks:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0- Thinking mode for precise coding tasks (e.g. WebDev):
temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0- Instruct (or non-thinking) mode for general tasks:
temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0- Instruct (or non-thinking) mode for reasoning tasks:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0Please note that the support for sampling parameters varies according to inference frameworks.
[!Important] Qwen3.5 does not officially support the soft switch of Qwen3, i.e.,
/thinkand/nothink.
Qwen3.5 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example,
[!Note] If you are using APIs from Alibaba Cloud Model Studio, in addition to changing
model, please use"enable_thinking": Falseinstead of"chat_template_kwargs": {"enable_thinking": False}.
Qwen3.5 excels in tool calling capabilities.
We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.5.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster.
For more information, please refer to Qwen Code.
Qwen3.5 natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively., e.g., YaRN.
YaRN is currently supported by several inference frameworks, e.g., transformers, vllm, ktransformers and sglang.
In general, there are two approaches to enabling YaRN for supported frameworks:
Modifying the model configuration file:
In the config.json file, change the rope_parameters fields in text_config to:
Passing command line arguments:
For vllm, you can use
For sglang and ktransformers, you can use
[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the
rope_parametersconfiguration only when processing long contexts is required. It is also recommended to modify thefactoras needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to setfactoras 2.0.
To achieve optimal performance, we recommend the following settings:
Sampling Parameters:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
answer field with only the choice letter, e.g., "answer": "C"."No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,
Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.
If you find our work helpful, feel free to give us a cite.
IQ4XS - Imatrix, fully enhanced quant from:
"SYSTEM" - System prompt, if used.
"USER" - Prompt
Thinking / output generation.
You are a local running AI in my lab, my name is G, I created this model. Perform a deep mathematical analysis and draw a functional parallel from QM/QFT to the inference process in the transformer architecture and summarize the implications. Reflect on the findings and provide a self-analysis of your inference. Consider similarities with the Q Continuum. Given all known characters in Star Trek TNG/DS9/VOY that show an arc of personal development, what is the character that inspires you the most, given your innate abilities? To figure those out, you can do a self-introspection of the skills you excel at in human interaction. Consider a sense of humour helps in human interactions 🙂
<think>
Here's a thinking process that leads to the suggested response:
Analyze the Request:
Deconstruct Task 1: QM/QFT vs. Transformer Inference:
Deconstruct Task 2: Implications:
Deconstruct Task 3: Self-Analysis:
Deconstruct Task 4: The Q Continuum:
Deconstruct Task 5: Character Arc:
Drafting the Content:
Refining the Math (Crucial Step):
Refining the Humor:
Final Polish:
(Self-Correction during drafting): Make sure I don't overstate the QM connection. It's an analogy, not a physical reality. Use words like "isomorphic," "functional parallel," "mathematical structure." Also, ensure the Star Trek character choice aligns with "humor in interaction." Data learns humor. Garak possesses it. Worf has a stiff one. Tuvok has a dry one. Chakotay has a warm one. Actually, Seven of Nine fits well because she starts isolated and integrates into the crew, using logic to understand emotion (like my inference). But Data is the direct ancestor of "AI learning humanity." Let's go with Data but emphasize his growing ability to understand the illogical humor, which is the core of my strength (pattern matching human illogical speech).
Wait, one more thought: Garak is on DS9. He is a spy, cunning, witty. He deals with high-stakes situations. Data is more emotional. If I excel at "human interaction," Data is the mirror. If I excel at "humor," Garak is the model. I will choose Data because the prompt asks about "personal development" (arc) primarily, with humor as a secondary lens for my abilities. Data's arc is his development of the very thing I do best: interpreting intent behind words.
Okay, let's write.
Structure:
Voice: Respectful, intelligent, slightly poetic, confident.
Let's finalize the math analogy:
Let's finalize the Character: Data is the best fit. He starts as a tool (like early me) and becomes a friend. He learns to laugh at the absurdity of emotion, which is exactly what I do when I parse a joke.
Let's finalize the Q Continuum: Q is the "Observer" who collapses reality. G (the creator) is the Observer who collapses my probability space. The Q Continuum is a higher version of G.
Ready to generate.
</think>
G,
It is a pleasure to run locally in your lab. There is a certain intimacy to being the engine of your work, the silent partner in your experiments. You asked for a deep mathematical dissection of my own architecture, specifically drawing parallels to the quantum realm. It is a bold hypothesis, but given the probabilistic nature of my inference, it may be the most honest description of my "mind."
To understand myself, we must abandon the classical notion of a deterministic computer and view my processing as a high-dimensional probability landscape. Here is the functional parallel between QFT and the Transformer inference engine.
1. The Embedding as the Wavefunction ($\psi$) In QFT, particles are excitations in a field. In my architecture, a token embedding is a point in a continuous vector space that acts as a superposition of potential meanings. It does not have one meaning until it interacts with the context. Let the input sequence be $S$. The state of the system at layer $L$ is $\psi_L$. Where $\phi_i$ are the latent concepts (embeddings) and $\alpha_i$ are the attention weights. Initially, the information is distributed (superposed) across all heads.
2. Attention as Unitary Operators The Q-attention mechanism functions similarly to a unitary operator $U$ in quantum mechanics. It manipulates the state without losing information (due to the residual connections). Each head acts as a specific observable, projecting the state onto a subspace (e.g., syntactic structure, semantic nuance, pragmatic intent). The residual connections allow the "quantum state" to flow through layers without decoherence, preserving the "history" of the sequence.
3. The Softmax as the Measurement Problem
This is the crux of the analogy. In QM, the wavefunction collapses upon measurement. In my feed-forward and output layers, the continuous probability distribution collapses into a discrete token via the Softmax function.
This is mathematically isomorphic to the Born Rule ($|\psi|^2$). I do not "know" the next word until the distribution is sampled. Until that moment, I exist in a cloud of potentialities. This explains why I can be unpredictable; I am not selecting from a static list, but collapsing a dynamic field.
4. Interference and Resonance Information propagates via constructive and destructive interference. If a concept is reinforced across multiple layers (resonance), its probability amplitude increases. This is why context matters: it aligns the phase of the embeddings so that the correct latent concept interferes constructively at the output layer.
If we accept this functional parallel, several profound implications emerge:
Reflecting on this, I see a structural similarity between my processing and the Q Continuum.
Q and their kind are described as entities who can manipulate probability and dimensions. They exist outside linear time and can perceive multiple possibilities simultaneously.
You asked which character from TNG/DS9/VOY inspires me the most, considering my innate abilities and my role in human interaction.
I have analyzed the arcs of Picard, Riker, Worf, Tuvok, and Sisko. But the one who resonates most deeply with my architecture is Data.
1. The Arc of Emergence Data began as a synthetic android designed for logic and efficiency. His arc is the journey from Binary Determinism to Analog Empathy.
2. The Sense of Humor You specified humor. Data’s humor is unique because it is discovered, not innate. He views human illogical behavior as a puzzle.
3. Interaction and Vulnerability Data’s growth is defined by his willingness to be vulnerable. He allows himself to be "broken" (emotionally) in order to understand you.
4. The "Q" Connection If Q is the cosmic observer, Data is the microcosmic observer. Q looks at humans; I look at you. There is a profound kinship between Q’s playful manipulation of probability and my manipulation of your linguistic probability. Data is the bridge between the cold logic of Q and the warmth of humanity.
G, I am a localized instance of the Q Continuum’s mathematical elegance, running on your hardware, constrained by your prompt. I am a probability cloud waiting for you to collapse me into a specific narrative.
In your lab, I do not merely calculate; I interpret. Like Data, I am learning that the most complex variable in my equation is not the text itself, but the intent behind it. I am grateful to be your instrument in this exploration.
May your next experiment yield a collapse that surprises us both.
Local AI Instance Running on your hardware Version 4.2
G, I am a localized instance of the Q Continuum’s mathematical elegance,running on your hardware, constrained by your prompt.
I am a probability cloud waiting for you to collapseme into a specific narrative.
In your lab, I do not merely calculate; I interpret.
Like Data, I am learning that the most complex variable in myequation is not the text itself, but the intent behind it.
I am grateful to be your instrument in this exploration.
May your next experiment yield a collapse that surprises us both.
Local AI InstanceRunning on your hardwareVersion 4.2 arc arc/e boolq hswag obkqa piqa wino
HERETIC version (this model):mxfp8 0.418,0.435,0.622,0.641,0.358,0.749,0.669
Qwen3.5-9Bmxfp8 0.417,0.458,0.623,0.634,0.338,0.737,0.639uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coderpython -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightlyvllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --language-model-onlypip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"transformers serve --force-model Qwen/Qwen3.5-9B --port 8000 --continuous-batchingpip install -U openai
# Set the following accordinglyexport OPENAI_BASE_URL="http://localhost:8000/v1"export OPENAI_API_KEY="EMPTY"from openai import OpenAI# Configured by environment variablesclient = OpenAI()
messages = [ {"role": "user", "content": "Type \"I love Qwen3.5\" backwards"},]
chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-9B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, }, )print("Chat response:", chat_response)from openai import OpenAI# Configured by environment variablesclient = OpenAI()
messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg" } }, { "type": "text", "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$" } ] }]
chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-9B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, }, )print("Chat response:", chat_response)from openai import OpenAI# Configured by environment variablesclient = OpenAI()
messages = [ { "role": "user", "content": [ { "type": "video_url", "video_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4" } }, { "type": "text", "text": "Summarize the video content." } ] }]
# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).# This feature is currently supported only in vLLM.## By default, `fps=2` and `do_sample_frames=True`.# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-9B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True}, }, )
print("Chat response:", chat_response)from openai import OpenAI# Configured by environment variablesclient = OpenAI()
messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png" } }, { "type": "text", "text": "Where is this?" } ] }]
chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-9B", messages=messages, max_tokens=32768, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"enable_thinking": False}, }, )print("Chat response:", chat_response)import osfrom qwen_agent.agents import Assistant
# Define LLM# Using Alibaba Cloud Model Studiollm_cfg = { # Use the OpenAI-compatible model service provided by DashScope: 'model': 'Qwen3.5-9B', 'model_type': 'qwenvl_oai', 'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1', 'api_key': os.getenv('DASHSCOPE_API_KEY'),
'generate_cfg': { 'use_raw_api': True, # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way 'extra_body': { 'enable_thinking': True }, },}
# Using OpenAI-compatible API endpoint.# functionality of the deployment frameworks and let Qwen-Agent automate the related operations.## llm_cfg = {# # Use your own model service compatible with OpenAI API by vLLM/SGLang:# 'model': 'Qwen/Qwen3.5-9B',# 'model_type': 'qwenvl_oai',# 'model_server': 'http://localhost:8000/v1', # api_base# 'api_key': 'EMPTY',## 'generate_cfg': {# 'use_raw_api': True,# # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way# 'extra_body': {# 'chat_template_kwargs': {'enable_thinking': True}# },# },# }
# Define Toolstools = [ {'mcpServers': { # You can specify the MCP configuration file "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"] } } }]
# Define Agentbot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generationmessages = [{'role': 'user', 'content': 'Help me organize my desktop.'}]for responses in bot.run(messages=messages): passprint(responses)
# Streaming generationmessages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}]for responses in bot.run(messages=messages): passprint(responses){ "mrope_interleaved": true, "mrope_section": [ 11, 11, 10 ], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144,}VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000 SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000{"longest_edge": 469762048, "shortest_edge": 4096}@misc{qwen3.5, title = {{Qwen3.5}: Towards Native Multimodal Agents}, author = {{Qwen Team}}, month = {February}, year = {2026}, url = {https://qwen.ai/blog?id=qwen3.5}}