10 ms·
Devstral
- solomatov 1y agoIt's very nice that it has the Apache 2.0 license, i.e. well understood license, instead of some "open weight" license with a lot of conditions.
- resource_waste 1y ago[flagged]
- solomatov 1y agoIMO, it's not about ethics, it's about legal risks. What if you want to fine tune a model on output related to your usage? Then my understanding is that all these derivatives need to be under the same license. What if G will change their prohibited use policy (the first line there is that they could update it from time to time)? There's really crazy stuff in terms of use of some services, what if G adds something in the same tune there which basically makes your application impossible. P.S. I am not a lawyer.
- orbisvicis 1y agoI'm not sure what you're trying to imply... only rogue software developers use devstral?
- dismalaf 1y agoIt's not about ethical or not, it's about risk to your startup. Ethics are super subjective (and often change based on politics). Apache means you own your own model, period.
- sofixa 1y ago> Ethics are super subjective (and often change based on politics). That's obviously not true. Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics". Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
- dragonwriter 1y ago> Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics". Ethics are entirely subjective, as is inherently true of anything that supports "should" statements, because to justify any should statement, you need another "should" statement, you can never rest should entirely on "is" (you can, potentially, reset any entire system of "should" one root "should" axiom, though in practice most systems have more than one root axiom.) And the process of coming to social consensus on a system of ethics is precisely politics. You can dislike that this is true, but it is true. > Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people. Understanding that ethics is subjective does not mean that one does not have a strong ethical framework that they adhere to. It just means that one understands the fundamental nature of ethics and the kind of propositions that ethical propositions inherently are. Understanding that ethics are subjective does not, in other words, imply the belief that all beliefs about ethics (or, a fortiori, matters that are inherently subjective more generally) are of equal moral/ethical merit.
- sofixa 1y ago> Ethics are entirely subjective That's obviously not true. Is it ethical to have slaves or kill children? No, objectively, it is not.
- Eupolemos 1y agoIt was in Rome. You are objectively using objectively wrong :-P (sorry, couldn't resist) Disclaimer: I agree that having slaves or killing children is very wrong.
- sneak 1y agoYou are getting morals and ethics confused. Ethics are an objective analysis framework. Morals are subjective. There are plenty of ethical frameworks where those examples you give objectively evaluate to “yes”.
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- simonw 1y agoWhat's different between the ethics of Mistral and Gemma?
- Philpax 1y agoI think their point was more that Gemma open models have restrictive licences, while some Mistral open models do not.
- Havoc 1y agoThey're all quite easy to strip of protections and I don't think anyone doing unethical stuff is big on following licenses anyway
- portaouflop 1y agoTIL Open Source is only used for unethical purposes
- YetAnotherNick 1y agoThe SWE bench is super impressive of model of any size. However just providing one benchmark results and having to do partnership with OpenHands seems like they focused too much on optimizing the number.
- AnhTho_FR 1y agoImpressive performance!
- ddtaylor 1y agoWow. I was just grabbing some models and I happened to see this one while I was messing with tool support in LLamaIndex. I have an agentic coding thing I threw together and I have been trying different models on it and was looking to throw ReAct at it to bring in some models that don't have tool support and this just pops into existence! I'm not able to get my agentic system to use this model though as it just says "I don't have the tools to do this". I tried modifying various agent prompts to explicitly say "Use foo tool to do bar" without any luck yet. All of the ToolSpec that I use are annotated etc. Pydantic objects and every other model has figured out how to use these tools.
- tough 1y agoyou can use constrained outptus for enforcing tool schemas any model can get it with a lil help
- abrowne2 1y agoCurious to check this out, since they say it can run on a 4090 / Mac with >32 GB of RAM.
- ddtaylor 1y agoI can run it without issue on a 6800 XT with 64GB of RAM.
- yencabulator 1y ago"Can run" is pretty easy, it's pretty small and quantized. It runs at 3.7 tokens/second on pure CPU with AMD 8945HS.
- sneak 1y agoI just ran it on a 24GB M2 air. Slow, but functional.
- simonw 1y agoThe first number I look at these days is the file size via Ollama, which for this model is 14GB https://ollama.com/library/devstral/tags https://ollama.com/library/devstral/tags I find that on my M2 Mac that number is a rough approximation to how much memory the model needs (usually plus about 10%) - which matters because I want to know how much RAM I will have left for running other applications. Anything below 20GB tends not to interfere with the other stuff I'm running too much. This model looks promising!
- lis 1y agoYes, I agree. I've just ran the model locally and it's making a good impression. I've tested it with some ruby/rspec gotchas, which it handled nicely. I'll give it a try with aider to test the large context as well.
- ericb 1y agoIn ollama, how do you set up the larger context, and figure out what settings to use? I've yet to find a good guide. I'm also not quite sure how I should figure out what those settings should be for each model. There's context length, but then, how does that relate to input length and output length? Should I just make the numbers match? 32k is 32k? Any pointers?
- lis 1y agoFor aider and ollama, see: https://aider.chat/docs/llms/ollama.html https://aider.chat/docs/llms/ollama.html Just for ollama, see: https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size https://github.com/ollama/ollama/blob/main/docs/faq.md#how-c... I’m using llama.cpp though, so I can’t confirm these methods.
- nico 1y agoAre you using it with aider? If so, how has your experience been?
- 1y ago
- gyudin 1y agoSuper weird benchmarks
- avereveard 1y agofrom what I gather it's finetuned to use OpenHand specifically so shows value on thsoe benchmark that target a whole system as a blackbox (i.e. agent + llm) more than directly target the llm input/outputs
- amarcheschi 1y agoYup the 1st comment says this https://www.reddit.com/r/LocalLLaMA/comments/1kryybf/mistralaidevstralsmall2505_hugging_face/ https://www.reddit.com/r/LocalLLaMA/comments/1kryybf/mistral...
- ics 1y agoMaybe someone here can suggest tools or at least where to look; what are the state-of-the-art models to run locally on relatively low power machines like a MacBook Air? Is there anyone tracking what is feasible given a machine spec? "Apple Intelligence" isn't it but it would be nice to know without churning through tests whether I should bother keeping around 2-3 models for specific tasks in ollama or if their performance is marginal there's a more stable all-rounder model.
- thatcherc 1y agoI would recommend just trying it out! (as long as you have the disk space for a few models). llama.cpp[0] is pretty easy to download and build and has good support for M-series Macbook Airs. I usually just use LMStudio[1] though - it's got a nice and easy-to-use interface that looks like the ChatGPT or Claude webpage, and you can search for and download models from within the program. LMStudio would be the easiest way to get started and probably all you need. I use it a lot on my M2 Macbook Air and it's really handy. [0] - https://github.com/ggml-org/llama.cpp https://github.com/ggml-org/llama.cpp [1] - https://lmstudio.ai/ https://lmstudio.ai/
- Etheryte 1y agoThis doesn't do anything to answer the main question of what models they can actually run.
- tuesdaynight 1y agoLM Studio will tell you if a specific model is small enough for your available RAM/VRAM.
- Miraste 1y agoThe best general model you can run locally is probably some version of Gemma 3 or the latest Mistral Small. On a Windows machine, this is limited by VRAM, since system RAM is too low-bandwidth to run models at usable speeds. On an M-series Mac, the system memory is on-die and fast enough to use. What you can run will be the total RAM, minus whatever MacOS uses and the space you want for other programs. To determine how much space a model needs, you look at the size of the quantized (lower precision) model on HuggingFace or wherever it's hosted. Q4_K_M is a good default. As a rough rule of thumb, this will be a little over half the size of the parameters, if they were in gigabytes. For Devstral, that's 14.3GB. You will also need 1-8GB more than that, to store the context. For example: A 32GB Macbook Air could use Devstral at 14.3+4GB, leaving ~14GB for the system and applications. A 16GB Macbook Air could use Gemma 3 12B at 7.3+2GB, leaving ~7GB for everything else. An 8GB Macbook could use Gemma 3 4B at 2.5GB+1GB, but this is probably not worth doing.
- bravura 1y agoAnd how do the results compare to hosted LLMs like Claude 3.7?
- resource_waste 1y agoEh, different usecase entirely. I don't really compare these.
- bufferoverflow 1y agoDifferent class. Same exact use case.
- ttoinou 1y agoFor which kind of coding would you use a subpar LLM ?
- troyvit 1y agoI'd use a "subpar" LLM for any coding practice where I want to do the bulk of the thinking and where I care about how much coal I'm burning. It's kind-of like asking, for which kind of road-trip would you use a Corolla hatchback instead of a Jeep Grand Wagoneer? For me the answer would be "almost all of them", but for others that might not be the case.
- ttoinou 1y agoIn that case examples of which trips would be interesting so we can take inspiration from you
- __MatrixMan__ 1y agoThe kind of coding that happens after the internet goes down. How important of a use case that is depends heavily on why the internet went down.
- kergonath 1y agoA LLM that I don’t host is a non-starter, so even a “subpar LLM” is better than someone’s cloud.
- ManlyBread 1y ago>Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an ideal choice for local deployment and on-device use This is still too much, a single 4090 costs $3k
- Uehreka 1y ago> a single 4090 costs $3k What a ripoff, considering that a 5090 with 32GB of VRAM also currently costs $3k ;) (Source: I just received the one I ordered from Newegg a week ago for $2919. I used hotstocks.io to alert me that it was available, but I wasn’t super fast at clicking and still managed to get it. Things have cooled down a lot from the craziness of early February.)
- IshKebab 1y agoThat's probably because the 5000 series seems to be a big let-down. It's pretty much identical to the 4000 series in efficiency; they've only increased performance by massively increasing power usage.
- hiatus 1y agoI receive NXDOMAIN for that hostname.
- jsheard 1y agoIt's hotstock.io, no plural.
- ttoinou 1y agoI can get the 5090 for 1700 euros on Amazon Spain. But there is 95% chance it is a scammy seller :P
- ranguna 1y agoI'm not sure where you are getting these prices from, but the cheapest 5090 I can find is 2755 on amazon Germany from the gigabyte store.
- oofbaroomf 1y agoThe SWE-Bench scores are very, very high for an open source model of this size. 46.8% is better than o3-mini (with Agentless-lite) and Claude 3.6 (with AutoCodeRover), but it is a little lower than Claude 3.6 with Anthropic's proprietary scaffold. And considering you can run this for almost free, this is a very extraordinary model.
- falcor84 1y agoJust to confirm, are you referring to Claude 3.7?
- oofbaroomf 1y agoNo. I am referring to Claude 3.5 Sonnet New, released October 22, 2024, with model ID claude-3-5-sonnet-20241022, colloquially referred to as Claude 3.6 Sonnet because of Anthropic's confusing naming.
- SkyPuncher 1y ago> colloquially referred to as Claude 3.6 Interesting. I've never heard this.
- simonw 1y agoIt's the reason Anthropic called their next release 3.7 Sonnet - the 3.6 version number was already being used by some in the community to refer to their 3.5v2.
- turing_complete 1y agobecause nobody says that
- NiloCK 1y agoAnthropic moved from 3.5, to 3.5(new), to 3.7. They skipped 3.6 because of usage in the community, and because 3.5(newer) probably passed some threshold of awfulness. People also use 3.5.1 to refer to 3.5(new)/3.6. The remaining difficulty now is when people refer to 3.5, without specifying (new) or (old). I find most unspecified references to 3.5 these days are actually to 3.6 / 3.5.1 / 3.5(new), which is confusing.
- dismalaf 1y agoIt's nice that Mistral is back to releasing actual open source models. Europe needs a competitive AI company. Also, Mistral has been killing it with their most recent models. I pay for Le Chat Pro, it's really good. Mistral Small is really good. Also building a startup with Mistral integration.
- gunalx 1y agomistral small 3.1 is also apache
- jadbox 1y agoBut how does it compare to deepcoder?
- CSMastermind 1y agoI don't believe the benchmarks they're presenting. I haven't tried it out yet but every model I've tested from Mistral has been towards the bottom of my benchmarks in a similar place to Llama. Would be very surprised if the real life performance is anything like they're claiming.
- Ancapistani 1y agoI've worked with other models from All Hands recently, and I believe they were based on Mistral. My general impression so far is that they aren't quite up to Claude 3.7 Sonnet, but they're quite good. More than adequate for an "AI pair coding assistant", and suitable for larger architectural work as long as you break things into steps for it.
- idonotknowwhy 1y agoI don't believe them either. We really have to test these ourselves imo. Qwen3 is a step backwards for me for example. And GLM4 is my current goto despite everyone saying it's "only good at html" The 70b cogito model is also really good for me but doesn't get any attention. I think it depends on our projects / languages we're using. Still looking forward to trying this one though :)
- gitroom 1y ago[dead]
- qwertox 1y agoMaybe the EU should cover the cost of creating this agent/model, assuming it really delivers what it promises. It would allow Mistral to keep focusing on what they do and for us it would mean that the EU spent money wisely.
- Havoc 1y ago>Maybe the EU should cover the cost of creating this model Wouldn't mind some of my taxpayer money flowing towards apache/mit licensed models. Even if just to maintain a baseline alternative & keep everyone honest. Seems important that we don't have some large megacorps run away with this.
- dismalaf 1y agoPretty sure the EU paid for some supercomputers that AI startups can use and Mistral is partner in that program.
- jgtrosh 1y agohttps://www.datacenterdynamics.com/en/news/french-data-center-firm-eclairion-raises-50m-hosts-mistral-ai-cluster/ https://www.datacenterdynamics.com/en/news/french-data-cente... this Eclairion colo currently being built south of Paris, mostly for Mistral has received some public money (incl. 3M€ from the region https://www.iledefrance.fr/toutes-les-actualites/ia-un-super-centre-de-calcul-soutenu-par-la-region-en-essonne https://www.iledefrance.fr/toutes-les-actualites/ia-un-super...)
- TZubiri 1y agoI feel this is part of a larger and very old business trend. But do we need 20 companies copying each other and doing the same thing? Like, is that really competition? I'd say competition is when you do something slightly different, but I guess it's subjective based on your interpretation of what is a commodity and what is proprietary. To my view, everyone is outright copying and creating commodity markets: OpenAI: The OG, the Coke of Modern AI Claude: The first copycat, The Pepsi of Modern AI Mistral: Euro OpenAI DeepSeek: Chinese OpenAI Grok/xAI: Republican OpenAI Google/MSFT: OpenAI clone as a SaaS or Office package. Meta's Llama: Open Source OpenAI etc...
- amarcheschi 1y agoI think llama is less open source than this mistral release
- nylonstrung 1y agoDeepseek and Mistral are both more open source than Lllama
- TZubiri 1y agoWill check it out. I like that we are all on the same page that Openness is a numerical value rather than a boolean, the challenge now is how to measure and define it, especially with ML
- kergonath 1y agoWell, Llama’s licence says that I am not allowed to use it. It does not take much to be more open then that.
- waldohatesyou 1y agoI don't think they're actually the same thing, I definitely feel like Claude is much better with code than ChatGPT is so there are clearly differences in the capabilities of these models. One analogy that I find helpful here is the idea that these AIs are like animals. Just like there are animals of the same family (meaning they're genetically related to some degree) they still adapt to different niches. I see all these AI companies ultimately creating models analogous to that. Some AIs will be good at coding (perhaps in a particular language or ecosystem), some at analyzing information and churning out a report for you, and some will be better at operating in physical spaces.
- johnQdeveloper 1y ago*For people without a 24GB RAM video card, I've got an 8GB RAM one running this model performs OK for simple tasks on ollama but you'd probably want to pay for an API for anything using a large context window that is time sensitive:* total duration: 35.016288581s load duration: 21.790458ms prompt eval count: 1244 token(s) prompt eval duration: 1.042544115s prompt eval rate: 1193.23 tokens/s eval count: 213 token(s) eval duration: 33.94778571s eval rate: 6.27 tokens/s total duration: 4m44.951335984s load duration: 20.528603ms prompt eval count: 1502 token(s) prompt eval duration: 773.712908ms prompt eval rate: 1941.29 tokens/s eval count: 1644 token(s) eval duration: 4m44.137923862s eval rate: 5.79 tokens/s Compared to an API call that finishes in about 20% of the time it feels a bit slow without the recommended graphics card and what not is all I'm saying. In terms of benchmarks, it seems unusually well tuned for the model size but I suspect its just a case of gaming the measurement by testing against it as part of the development of the model which is not bad in and of itself since I suspect every LLM who is in this space marketed to IT folks does the same thing tbh so its objective enough given that as a rough gauge of "Is this usable?" without heavy time expense testing it.
- throwaway314155 1y ago> For people without a 24GB RAM video card, I've got an 8GB RAM one running What're you using for this? llama.cpp? Have a 12GB card (rtx 4070) i'd like to try it on.
- johnQdeveloper 1y agohttps://ollama.com/library/devstral https://ollama.com/library/devstral https://ollama.com/ https://ollama.com/ I believe its just a HTTP wrapper and terminal wrapper around llama.cpp with some modifications/fork.
- throwaway314155 1y agoDoes ollama have support for cpu offloading?
- christophilus 1y agoWhat hardware are y'all using when you run these things locally? I was thinking of pre ordering the Framework desktop[0] for this purpose, but I wouldn't mind having a decent laptop that could run it (ideally Linux). [0] https://frame.work/desktop https://frame.work/desktop
- snitty 1y agoI think your options are generally: 0) A desktop PC with one or more graphics cards, or 1) A Mac with Apple Silicon
- tripplyons 1y agoAll Hands AI has instructions for running Devstral locally on a MacBook using LMStudio: https://docs.all-hands.dev/modules/usage/llms/local-llms#serve-the-model-on-your-macbook https://docs.all-hands.dev/modules/usage/llms/local-llms#ser... The same page also gives instructions for running the model through VLLM on a GPU, but it doesn't seem like it supports quantization, so it may require multiple GPUs since the instructions say "with at least 2 GPUs".
- klooney 1y agoAMD is going to be off the beaten path, you're likely to have more success/less boring plumbing trouble with nVidia.
- lolinder 1y agoDoes Nvidia have integrated memory options that allow you to get up to 64GB+ of VRAM without stringing together a bunch of 4090s? For local LLMs Apple Silicon has really shown the value of shared memory, even if that comes at the cost of raw GPU power. Even if it's half the speed of an array of GPUs, being able to load the mid-sized models at all is a huge plus.
- kookamamie 1y agoNot quite, but I do have an Ada 6000, which has 48GB.
- anonym29 1y agoI know it's not the recommended runner (OpenHands), but running this on Cline (ollama back-end), it seemed absolutely atrocious at file reading and tool calling.
- deleted 1y ago[deleted]
- jwr 1y agoMy experience with LLMs seems to indicate that the benchmark numbers are more and more detached from reality, at least my reality. I tested this model with several of my Clojure problems and it is significantly worse than qwen3:30b-a3b-q4_K_M. I don't know what to make of this. I don't trust benchmarks much anymore.
- NitpickLawyer 1y agoHow did you test this? Note that this is not a regular coding model (i.e. write a function that does x). This is a fine-tuned model specifically post-trained on a cradle (open hands, ex open devin). So their main focus was to enable the "agentic" flows, with tool use, where you give the model a broad task (say a git ticket) and it starts by search_repo() or read_docs(), followed by read_file() in your repo, then edit_file(), then run_tests() and so on. It's intended to first solve those problems. They suggest using it w/ open hands for best results. Early reports from reddit say that it also works in cline, while other stronger coding models had issues (they were fine-tuned more towards a step-by-step chat with a user). I think this distinction is important to consider when testing.
- desdenova 1y agoI did a very simple tool calling test and it was simply unable to call the tool and use the result. Maybe it's specialized to use just a few very specific tools? Is there some documentation on how to actually set it up without requiring some weird external platform?
- tasuki 1y ago> "write a function that does x" Which model is optimized to do that? This is what I want out of LLMs! And also talking high level architecture (without any code) and library discovery, but I guess the general talking models are good for that...
- jwr 1y agoI didn't actually even test tool calling. I have two test cases that I use for all models: one is a floating-point equality function, which is quite difficult to get right, and another is a core.async pack-into-batches! function which has the following docstring: "Take items from `input-ch` and group them into `batch-size` vectors. Put these onto `output-ch`. Once items start arriving, if `batch-size` items do not arrive within `inactivity-timeout`, put the current incomplete batch onto `output-ch`. If an anomaly is received, passes it on to `output-ch` and closes all channels. If `input-ch` is closed, closes `output-ch`. If `flush-predicate-fn` is provided, it will get called with two parameters: the currently accumulated batch (guaranteed to have at least one item) and the next item. If the function returns a truthy value, the batch will get flushed immediately. If `convert-batch-fn` is provided, it will get called with the currently accumulated batch (guaranteed to have at least one item) and its return value will be put onto `output-ch`. Anomalies bypass `convert-batch-fn` and get put directly onto `output-ch` (which gets closed immediately afterwards)." In other words, not obvious. I ask the model to review the code and tell me if there are improvements that can be made. Big (online) models can do a pretty good job with the floating point equality function, and suggest something at least in the ballpark for the async code. Small models rarely get everything right, but some of their observations are good.
- thih9 1y ago> Devstral excels at using tools to explore codebases As an AI and vibe coding newbie, how does that work? E.g. how would I use devstral and ollama and instruct it to use tools? Or would I need some other program as well?
- desdenova 1y agoIn the Ollama API, you use the "tools" parameter to describe the available tools to the model, then use the "tool_calls" from the response to call the functions and send the results back to the model using "role": "tool". Most of this is handled very easily by the ollama-python library, so you can integrate tool calling very simply in any script. That said, this specific model was unable to call the functions and use the results in my "hello world" tests, so it seems it expects a few very specialized tools to be provided, which are defined by that platform they're advertising. Right now the best tool calling model I've used is still qwen3, it works very reliably, and I can give it any ability I want and it'll use it when expected, even in /no_think mode.
- mekpro 1y agoit can use tool to explore directory like ls grep out of the box.
- screye 1y agoWhat's the play for smaller base model training companies like Mistral ? Mistral's positioning as the European alternative doesn't seem to be sticking. Acquisition seems tricky given how inflection, character.ai and stability have got carved out. The big acquisition bucks are going to product companies (windsurf) They could pivot up the stack, but then they'd be starting from scratch with a team that's ill-suited for product development. The base model offerings from pretraining companies have been surprisingly myopic. Deepmind seems to be the only one going past the obvious "content gen/coding automation" verticals. There's a whole world out there. LLM product companies are fast acquiring pieces of the real money pie and smaller pretraining companies are getting left out. ______ edit: my comment rose to the top. It's early in the morning. Injecting a splash of optimism. LLMs are hard, and giants like Meta are struggling to make steady progress. Mistrals models are cheap, competent, open-source-ish and don't come with AGI-is-imminent baggage. Good enough for me. To my own question: They have a list of target industries at the top. https://mistral.ai/solutions#industry https://mistral.ai/solutions#industry Good luck to them.
- gunalx 1y agoI actually think the underdog, cheap, but still capable independent european alternative is a decent selling point. They have also branched out into specialised models, and custom training. as well as their ocr service.
- twotwotwo 1y agoAny company in this space outside of the top few should be contributing to the open-source tools (Aider, OpenHands, etc.); that is a better bet than making your own tools from scratch to compete with ones from much bigger teams. A couple folks making harnesses work better with your model might yield improvements faster than a lot of model-tuning work, and might also come out of the process with practical observations about what to work on in the next spin of the model. Separately, deploying more autonomous agents that just look at an issue or such just seems premature now. We've only just gotten assisted flows kind-of working, and they still get lost--get stuck on not-hard-for-a-human debugging tasks, implement Potemkin 'fixes', forget their tools, make unrelated changes that sometimes break stuff, etc.--in ways that imply that flow isn't fully-baked yet. Maybe the main appeal is asynchrony/potential parallelism? You could tackle that different ways, though. And SWEBench might be a good benchmark still (focus on where you want to be, even if you aren't there yet), but that doesn't mean it represents the most practical way to use these tools day-to-day currently.