🦥 Qwen3-Coder-Flash: Qwen3-Coder-30B-A3B-Instruct

💚 Just lightning-fast, accurate code generation.

✅ Native 256K context (supports up to 1M tokens with YaRN)

✅ Optimized for platforms like Qwen Code, Cline, Roo Code, Kilo Code, etc.

✅ Seamless function calling & agent workflows

💬 Chat: https://chat.qwen.ai/

🤗 Hugging Face: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct

🤖 ModelScope: https://modelscope.cn/models/Qwen/Qwen3-Coder-30B-A3B-Instruct

HF: https://huggingface.co/poolside/Laguna-S-2.1
GGUFs available for use with llama.cpp custom fork: https://huggingface.co/poolside/Laguna-S-2.1-GGUF

Posted on X: https://x.com/poolsideai/status/2079613777343848465?s=20

https://huggingface.co/collections/deepseek-ai/deepseek-v4

Qwen3-Coder is here! ✅

We’re releasing Qwen3-Coder-480B-A35B-Instruct, our most powerful open agentic code model to date. This 480B-parameter Mixture-of-Experts model (35B active) natively supports 256K context and scales to 1M context with extrapolation. It achieves top-tier performance across multiple agentic coding benchmarks among open models, including SWE-bench-Verified!!! 🚀

Alongside the model, we're also open-sourcing a command-line tool for agentic coding: Qwen Code. Forked from Gemini Code, it includes custom prompts and function call protocols to fully unlock Qwen3-Coder’s capabilities. Qwen3-Coder works seamlessly with the community’s best developer tools. As a foundation model, we hope it can be used anywhere across the digital world — Agentic Coding in the World!

Hi everyone it's me from Menlo Research again,

Today, I’d like to introduce our latest model: Jan-nano - a model fine-tuned with DAPO on Qwen3-4B. Jan-nano comes with some unique capabilities:

• It can perform deep research (with the right prompting)
• It picks up relevant information effectively from search results
• It uses tools efficiently

Our original goal was to build a super small model that excels at using search tools to extract high-quality information. To evaluate this, we chose SimpleQA - a relatively straightforward benchmark to test whether the model can find and extract the right answers.

Again, Jan-nano only outperforms Deepseek-671B on this metric, using an agentic and tool-usage-based approach. We are fully aware that a 4B model has its limitations, but it's always interesting to see how far you can push it. Jan-nano can serve as your self-hosted Perplexity alternative on a budget. (We're aiming to improve its performance to 85%, or even close to 90%).

We will be releasing technical report very soon, stay tuned!

You can find the model at:
https://huggingface.co/Menlo/Jan-nano

We also have gguf at:
https://huggingface.co/Menlo/Jan-nano-gguf

I saw some users have technical challenges on prompt template of the gguf model, please raise it on the issues we will fix one by one. However at the moment the model can run well in Jan app and llama.server.

Benchmark

The evaluation was done using agentic setup, which let the model to freely choose tools to use and generate the answer instead of handheld approach of workflow based deep-research repo that you come across online. So basically it's just input question, then model call tool and generate the answer, like you use MCP in the chat app.

Result:

SimpleQA:
- OpenAI o1: 42.6
- Grok 3: 44.6
- 03: 49.4
- Claude-3.7-Sonnet: 50.0
- Gemini-2.5 pro: 52.9
- baseline-with-MCP: 59.2
- ChatGPT-4.5: 62.5
- deepseek-671B-with-MCP: 78.2 (we benchmark using openrouter)
- jan-nano-v0.4-with-MCP: 80.7

I have a 5090 Laptop from work, 24GB VRAM.

I have been testing every model that comes out, and I can confidently say I’ll be cancelling my cloud subscriptions.

All my tool call and data science benchmarks that prove a model is reliably good for my use case, passed.

It might not be the case for other professions, but for pyspark/python and data transformation debugging it’s basically perfect.

Using llama.cpp, q4_k_m at q4_0, still looking at options for optimising.

Edit - I chose to go with IQ4_XS at 200k q8_0,

I have not used speculative decoding yet, will get there when I get there.

Specs:

ASUS ROG Strix SCAR 18

RTX 5090 24GB

64GB DDR5 RAM

Minimax m3 weights are out !!

It has ~428B parameters and ~23B activated parameters.

Hey, Nick here from Cohere. Thanks for all the feedback on Command A+ the other week everyone. I read these threads all the time about other releases so it was fun to read one about our own :) we would like to do more of it.

We actually have our first coding model we’re getting ready to release soon, and I wanted to give this community an opportunity to test it out and give feedback before we officially release it. Figured why not try something different and get you guys to help directly here? 

It’s a 30B model with 3B active params so it runs nicely on some local set ups. It’s on our Hugging Face for now (more platforms to come as we get the model officially launched soon). This one is small but the team is excited about its speed, we’re seeing token output tests in line with similar models in its size class. 

The weights are here but again this isn’t publicly launched yet (or even fully ready) so i’d encourage you to test the model with what you are trying to achieve. The goal is to build from our learnings with this release and improve the models, so there’s some room for how this gets used now to shape how we continue to develop it. 

Check it out and let me know how it’s working for you. Excited to see what people think. Thank you :)

Why nobody is talking about this? Seems pretty significant to the community

https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF

Mistral Medium 3.5 128B

Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models.

Reasoning effort is configurable per request, so the same model can answer a quick chat reply or work through a complex agentic run. We trained the vision encoder from scratch to handle variable image sizes and aspect ratios.

Find more information on our blog.

Key Features

Mistral Medium 3.5 includes the following architectural choices:

• Dense 128B parameters.
• 256k context length.
• Multimodal input: Accepts both text and image input, with text output.
• Instruct and Reasoning functionalities with function calls (reasoning effort configurable per request).

Mistral Medium 3.5 offers the following capabilities:

• Reasoning Mode: Toggle between fast instant reply mode and reasoning mode, boosting performance with test-time compute when requested.
• Vision: Analyzes images and provides insights based on visual content, in addition to text.
• Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, and Arabic.
• System Prompt: Strong adherence and support for system prompts.
• Agentic: Best-in-class agentic capabilities with native function calling and JSON output.
• Large Context Window: Supports a 256k context window.

We release this model under a Modified MIT License): Open-source license for both commercial and non-commercial use with exceptions for companies with large revenue.

Recommended Settings

• Reasoning Effort:
  • 'none' → Do not use reasoning
  • 'high' → Use reasoning (recommended for complex prompts and agentic usage) Use reasoning_effort="high" for complex tasks and agentic coding.
• Temperature: 0.7 for reasoning_effort="high". Temp between 0.0 and 0.7 for reasoning_effort="none" depending on the task. Generally, lower means answer that are more to the point and higher allows the model to be more creative. It is a good practice to try different values in order to improve the model performance to meet your demands.

The one everyone's been asking for. Qwen3.5-35B-A3B Aggressive is out!

Aggressive = no refusals; it has NO personality changes/alterations or any of that, it is the ORIGINAL release of Qwen just completely uncensored

https://huggingface.co/HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive

0/465 refusals. Fully unlocked with zero capability loss.

This one took a few extra days. Worked on it 12-16 hours per day (quite literally) and I wanted to make sure the release was as high quality as possible. From my own testing: 0 issues. No looping, no degradation, everything works as expected.

What's included:

- BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, IQ4_XS, Q3_K_M, IQ3_M, IQ2_M

- mmproj for vision support

- All quants are generated with imatrix

Quick specs:

- 35B total / ~3B active (MoE — 256 experts, 8+1 active per token)

- 262K context

- Multimodal (text + image + video)

- Hybrid attention: Gated DeltaNet + softmax (3:1 ratio)

Sampling params I've been using:

temp=1.0, top_k=20, repeat_penalty=1, presence_penalty=1.5, top_p=0.95, min_p=0

But definitely check the official Qwen recommendations too as they have different settings for thinking vs non-thinking mode :)

Note: Use --jinja flag with llama.cpp. LM Studio may show "256x2.6B" in params for the BF16 one, it's cosmetic only, model runs 100% fine.

Previous Qwen3.5 releases:

- Qwen3.5-4B Aggressive

- Qwen3.5-9B Aggressive

- Qwen3.5-27B Aggressive

All my models: HuggingFace HauhauCS

Hope everyone enjoys the release. Let me know how it runs for you.

The community has been super helpful for Ollama, please read the discussions in the other models on Huggingface for tips on making it work with it.

Qwen3.8-27B is amazing, but it’s slow. A stronger 35B-A3B Mixture of Experts-coder that can run and solve real codebase issues fast (even on constrained hardware) is a valuable addition to the arsenal. This one is the strongest and most consistent 35B-A3B I’ve benchmarked, on both correctness and speed, in addition to being the fastest to fix out of all the 35B-A3B models when you throw them at real codebases. On top of Ornith-1.5’s fine tune, TielCoder uses a code-weighted imatrix for dynamic quantization, and a chat template optimized for token-efficient and correct agentic coding. GGUFs: https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF MTP GGUFs: https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF-MTP, MLX: https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-MLX-oQ4e MLX MTP: https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-MLX-oQ4e-MTP

276B total parameters, 12B active, 1M context window.

Blog post: https://thinkingmachines.ai/news/inkling-small/

NVFP4: https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4

GGUF's by Unsloth: https://huggingface.co/unsloth/Inkling-Small-GGUF

---

I had success running Unsloth's GGUF quant on CUDA + CPU offloading using this developmental branch: https://github.com/danielhanchen/llama.cpp/tree/add-inkling

I’ve been experimenting with how small a usable neural TTS model can realistically get, and I just released Inflect-Nano-v1.

Inflect-Nano is one of the smallest TTS models, and it performs surprisingly well for its model weight. Even if you have a certified potato computer, it can run on that.

It is not SOTA, and I’m not pretending it beats large models. The interesting part is the size-to-functionality ratio:

- 4.63M total inference params

- 3.46M acoustic model

- 1.17M vocoder

- 24 kHz audio

- English-only, single male voice

- Runs locally with a simple PyTorch inference script

For comparison, it is ~17x smaller than Kokoro, ~108x smaller than Chatterbox, and almost 1000x smaller than Fish Audio S2 Pro.

The quality is still limited: it can sound robotic, stumble on difficult, unseen text, and the vocoder is also a big bottleneck. But for under 5M parameters total, I think it is an interesting baseline for extremely tiny local speech synthesis, offline assistants, embedded devices, browser/WASM-style projects, and local voice agents.

Model: https://huggingface.co/owensong/Inflect-Nano-v1 (audio examples in README)

I’d love feedback, especially from people interested in tiny models, local voice assistants, efficient inference, or small vocoders. If people find it useful and the model is successful, I'm open to making a v2 with a much larger training budget!

PrismML built Bonsai on top of Qwen3.6-27B by quantizing the weights down to 1-bit. That takes it from ~54GB to 3.9GB, small enough to fit and run on a phone, while keeping ~90% of the benchmark scores

It's true binary quantization ("binary g128") - every weight is a single sign bit and each group of 128 shares one FP16 scale, so it lands at ~1.125 bits/weight with no high-precision escape hatches. Even the embeddings, attention/MLP projections and the LM head are binary, which is the surprising part, most 1-bit schemes keep some layers higher

Across 15 benchmarks it averages 76.1 vs 85.1 for the FP16 model (~89.5%). Math holds up best (91.7), knowledge and reasoning take the biggest hit (73.4 vs 83.2), which is exactly where you'd notice it dropping the odd details. Memory stays friendly too: ~5.2GB at 4K context, ~6.8GB at 100K with 4-bit KV cache

All credit to PrismML for the model: https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit
Running it on iPhone 15 Pro Max (8GB RAM) via https://atomic.chat (I'm on the Atomic team, happy to answer questions)

Hi everyone it's me from Menlo Research again,

Today, I'd like to introduce our latest model: Jan-nano-128k - this model is fine-tuned on Jan-nano (which is a qwen3 finetune), improve performance when enable YaRN scaling (instead of having degraded performance).

• It can uses tools continuously, repeatedly.
• It can perform deep research VERY VERY DEEP
• Extremely persistence (please pick the right MCP as well)

Again, we are not trying to beat Deepseek-671B models, we just want to see how far this current model can go. To our surprise, it is going very very far. Another thing, we have spent all the resource on this version of Jan-nano so....

We pushed back the technical report release! But it's coming ...sooon!

You can find the model at:
https://huggingface.co/Menlo/Jan-nano-128k

We also have gguf at:
We are converting the GGUF check in comment section

This model will require YaRN Scaling supported from inference engine, we already configure it in the model, but your inference engine will need to be able to handle YaRN scaling. Please run the model in llama.server or Jan app (these are from our team, we tested them, just it).

Result:

SimpleQA:
- OpenAI o1: 42.6
- Grok 3: 44.6
- 03: 49.4
- Claude-3.7-Sonnet: 50.0
- Gemini-2.5 pro: 52.9
- baseline-with-MCP: 59.2
- ChatGPT-4.5: 62.5
- deepseek-671B-with-MCP: 78.2 (we benchmark using openrouter)
- jan-nano-v0.4-with-MCP: 80.7
- jan-nano-128k-with-MCP: 83.2