#codellama — Public Fediverse posts

Một thảo luận trên Reddit hỏi về API LLM tốt nhất để dịch hơn 300k file code sang ngôn ngữ lập trình khác, với chi phí hợp lý. Gợi ý: OpenAI GPT‑4, Azure OpenAI, Anthropic Claude, Google PaLM, và các mô hình mã nguồn mở như CodeLlama, StarCoder. #LLM #APIs #CodeTranslation #OpenAI #Azure #Anthropic #Google #StarCoder #CodeLlama #Coding #Translation #VietNam #MãNguồnMở #CôngNghệ

https://www.reddit.com/r/LocalLLaMA/comments/1or8bcf/best_llm_api_for_mass_code_translation/

#Codellama is hilarious:

>>> So you are not "codellama" and your job is not to write code like a python program? I think the reality is, codellama does not know python programming.
...
🤔 Ah, I see! You're correct in saying that my purpose is not to write
Python programs or provide coding assistance. As a responsible AI language model, my primary goal is to assist with general knowledge-based tasks and provide helpful information on a wide range of topics. 🤖

#llm

Researchers have uncovered a new supply chain attack called #Slopsquatting where threat actors exploit hallucinated, non-existent package names generated by #AI coding tools like #GPT4 and #CodeLlama

These believable yet fake packages (amounting to 19.7% or 205,000 packages), recommended in test samples were found to be fakes., can be registered by attackers to distribute malicious code.

Open-source models -- like #DeepSeek and #WizardCoder -- hallucinated more frequently, at 21.7% on average, compared to the commercial ones (5.2%) like GPT 4.

We Have a Package for You! A Comprehensive Analysis of Package Hallucinations
by Code Generating LLMs (PDF) https://arxiv.org/pdf/2406.10279

#Ollama models:
➡️ #CodeLlama offers versatile development assistance with advanced pattern recognition and infilling capabilities.
➡️ Qwen 2.5 Coder transforms debugging by providing intelligent solutions to root causes.
➡️ WizardLM2 excels in rapid prototyping and multilingual development.
➡️ Mistral 7B is a lightweight model that delivers impressive performance with efficient architecture.

https://insights.codegpt.co/best-ollama-model-for-coding?form=MG0AV3

#AI #SoftwareDevelopment #CodeLlama #QwenCoder #WizardLM2 #Mistral7B #Programming

#Ollama models:
➡️ #CodeLlama offers versatile development assistance with advanced pattern recognition and infilling capabilities.
➡️ Qwen 2.5 Coder transforms debugging by providing intelligent solutions to root causes.
➡️ WizardLM2 excels in rapid prototyping and multilingual development.
➡️ Mistral 7B is a lightweight model that delivers impressive performance with efficient architecture.

https://insights.codegpt.co/best-ollama-model-for-coding?form=MG0AV3

#AI #SoftwareDevelopment #CodeLlama #QwenCoder #WizardLM2 #Mistral7B #Programming

#Ollama models:
➡️ #CodeLlama offers versatile development assistance with advanced pattern recognition and infilling capabilities.
➡️ Qwen 2.5 Coder transforms debugging by providing intelligent solutions to root causes.
➡️ WizardLM2 excels in rapid prototyping and multilingual development.
➡️ Mistral 7B is a lightweight model that delivers impressive performance with efficient architecture.

https://insights.codegpt.co/best-ollama-model-for-coding?form=MG0AV3

#AI #SoftwareDevelopment #CodeLlama #QwenCoder #WizardLM2 #Mistral7B #Programming

#Ollama models:
➡️ #CodeLlama offers versatile development assistance with advanced pattern recognition and infilling capabilities.
➡️ Qwen 2.5 Coder transforms debugging by providing intelligent solutions to root causes.
➡️ WizardLM2 excels in rapid prototyping and multilingual development.
➡️ Mistral 7B is a lightweight model that delivers impressive performance with efficient architecture.

https://insights.codegpt.co/best-ollama-model-for-coding?form=MG0AV3

#AI #SoftwareDevelopment #CodeLlama #QwenCoder #WizardLM2 #Mistral7B #Programming

#Ollama models:
➡️ #CodeLlama offers versatile development assistance with advanced pattern recognition and infilling capabilities.
➡️ Qwen 2.5 Coder transforms debugging by providing intelligent solutions to root causes.
➡️ WizardLM2 excels in rapid prototyping and multilingual development.
➡️ Mistral 7B is a lightweight model that delivers impressive performance with efficient architecture.

https://insights.codegpt.co/best-ollama-model-for-coding?form=MG0AV3

#AI #SoftwareDevelopment #CodeLlama #QwenCoder #WizardLM2 #Mistral7B #Programming

What is the most advanced auto #coding #AI, out there right now, in your opinion?

And i don't just mean coding #Assistants like #ChatGpt, #Copilot, #CodeLLama, #Tabnine, #Cody etc.

Gerade hyperparameter Tuning mit optuna entdeckt. Super Tool :)

Findet auch der Server, der jetzt die nächsten 5 Tage am Stück mein Modell tuned ✌️😅

Grüße gehen raus nach Mexico...

#ml #ai #codellama #locallama #thesis #hashtag #brum

Running LLMs locally with 3 lines of code 😎
https://ollama.com/
#ollama #llama2 #codellama #llm #ai #python

I want to recommend a web interface for communication with open source LLM — HuggingChat. On the site, you can use Mixtral-8x7B-Instruct-v0.1, #CodeLlama 70b, #Llama 2 70b, and a couple of smaller models as well.

Also, the site has its own version of #GPTs — assistants. Since the #HuggingChat service is significantly less known than ChatGPT, there aren’t as many assistants yet. But their presence alone pleases me greatly.

https://huggingface.co/chat/

#ai #llm

Meta Code Llama 70B – Das nächste Level für automatisierten Code

#CodeLlama #KuenstlicheIntelligenz #CodeGenerierung #Programmierung #AI #MachineLearning #SoftwareEntwicklung #Python #TechInnovation #artificialintelligence

https://kinews24.de/meta-code-llama-70b/

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

Code Llama: Ein Llama lernt programmieren | heise online
https://heise.de/-9537794 #Codegenerierung #CodeLlama

Code Llama: Ein Llama lernt programmieren | heise online
https://heise.de/-9537794 #Codegenerierung #CodeLlama

Code Llama: Ein Llama lernt programmieren | heise online
https://heise.de/-9537794 #Codegenerierung #CodeLlama

Code Llama: Ein Llama lernt programmieren | heise online
https://heise.de/-9537794 #Codegenerierung #CodeLlama

Code Llama: Ein Llama lernt programmieren | heise online
https://heise.de/-9537794 #Codegenerierung #CodeLlama

I'm surprised neither #Copilot nor #ChatGPT4 can write #Quicksort in #REXX. Might be a good testbed for learning how to fine-tune, e.g., #CodeLlama. (Assuming CL doesn't know REXX either.) How much code for syntax to "take"?

Messed around with ollama last night and put my wired ethernet connection to good use. Downloaded mistral, llama2, and codellama just to test out some different models. Things aren't as slow as you'd expect and are completely runnable on my m1 macbook air. Pretty exciting to be self-hosting a LLM.

#llm #ollama #selfhost #llama2 #mistral #mistralai #m1 #applem1 #codellama

If you are interested in using #codellama or #wizardcoder with #vscode, I have a foss #vscode / #vscodium extension which allows for fairly flexible code editing using these models.

https://github.com/balisujohn/localpilot

Since it uses #text-generation-webui as a backend, it's compatible with machines with and without GPUs and doesn't require a Docker server like Fauxpilot.

This demo is local CPU inference only with a laptop i7, with the model WizardCoder python 13b.

Is there some thePyrateBay for #NeuralNetworks? Want to test #CodeLlama but Meta require some jumps over hoop to download it

#LLM

Large language model Code Llama released by Meta https://www.fosslife.org/meta-announces-code-llama #AI #Llama2 #CodeLlama #OpenSource #Meta #programming #SoftwareDevelopment #license #research #CommercialUse

Large language model Code Llama released by Meta https://www.fosslife.org/meta-announces-code-llama #AI #Llama2 #CodeLlama #OpenSource #Meta #programming #SoftwareDevelopment #license #research #CommercialUse

Large language model Code Llama released by Meta https://www.fosslife.org/meta-announces-code-llama #AI #Llama2 #CodeLlama #OpenSource #Meta #programming #SoftwareDevelopment #license #research #CommercialUse

Large language model Code Llama released by Meta https://www.fosslife.org/meta-announces-code-llama #AI #Llama2 #CodeLlama #OpenSource #Meta #programming #SoftwareDevelopment #license #research #CommercialUse

Large language model Code Llama released by Meta https://www.fosslife.org/meta-announces-code-llama #AI #Llama2 #CodeLlama #OpenSource #Meta #programming #SoftwareDevelopment #license #research #CommercialUse

Finetuned code-llama beats GPT-4. They have no moat, indeed.

https://www.phind.com/blog/code-llama-beats-gpt4

#Llama #CodeLlama #GPT4 #AI #ChatGPT

Downloading #codellama full weights cause I’m a data hoarder.

[Paper] ➡️ https://scontent.fxds1-1.fna.fbcdn.net/v/t39.2365-6/369856151_1754812304950972_1159666448927483931_n.pdf?_nc_cat=107&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=BnkB4kcpz5AAX9Gjp6Y&_nc_ht=scontent.fxds1-1.fna&oh=00_AfDD0H0yCBeeXTzZXxQlVvaQxQgR46EsLaUki91YLBP9rQ&oe=64EEAC4F

#LLaMa #LLama2 #MetaAI

Meta introduces Code Llama, an AI tool aimed at faster coding and debugging - Enlarge (credit: Getty Images | Benj Edwards)

Meta is adding a... - https://arstechnica.com/?p=1963185 #largelanguagemodels #machinelearning #aicodingtools #textsynthesis #aiassistants #codellama #biz⁢ #llama2 #metaai #llama #tech #meta #ai

Buongiorno dalla @EU_Commission perché oggi è il giorno dell'entrata in vigore del Digital Markets Act (DMA) e quindi torniamo un po' a vedere di che si tratta grazie al bell'articolo di Agenda Digitale, poi ci ricordiamo di essere un podcast di programmazione e quindi vediamo la nuova release 0.8.0 di Bun, e poi il nuovo Code Llama di Meta e un trittico di notizie economiche con numeri che fanno girare la testa sempre in campo AI.

#dma #eu #bun #codellama #ai

https://youtu.be/an_M7vt1KP4

Code Llama running locally against a git repository with Aider. The open source models just keep getting better.

#LLM #Llama #CodeLlama #Aider #ML #AI

Meta today open-sourced #Code #Llama, a machine learning system that can generate and explain code in natural language — specifically English.

Akin to GitHub #Copilot and Amazon #CodeWhisperer, as well as open source AI-powered code generators like #StarCoder, #StableCode and #PolyCoder, #CodeLlama can complete code and debug existing code across a range of programming languages

https://techcrunch.com/2023/08/24/meta-releases-code-llama-a-code-generating-ai-model/

Meta today open-sourced #Code #Llama, a machine learning system that can generate and explain code in natural language — specifically English.

Akin to GitHub #Copilot and Amazon #CodeWhisperer, as well as open source AI-powered code generators like #StarCoder, #StableCode and #PolyCoder, #CodeLlama can complete code and debug existing code across a range of programming languages

https://techcrunch.com/2023/08/24/meta-releases-code-llama-a-code-generating-ai-model/

Meta today open-sourced #Code #Llama, a machine learning system that can generate and explain code in natural language — specifically English.

Akin to GitHub #Copilot and Amazon #CodeWhisperer, as well as open source AI-powered code generators like #StarCoder, #StableCode and #PolyCoder, #CodeLlama can complete code and debug existing code across a range of programming languages

https://techcrunch.com/2023/08/24/meta-releases-code-llama-a-code-generating-ai-model/

Meta today open-sourced #Code #Llama, a machine learning system that can generate and explain code in natural language — specifically English.

Akin to GitHub #Copilot and Amazon #CodeWhisperer, as well as open source AI-powered code generators like #StarCoder, #StableCode and #PolyCoder, #CodeLlama can complete code and debug existing code across a range of programming languages

https://techcrunch.com/2023/08/24/meta-releases-code-llama-a-code-generating-ai-model/

Meta today open-sourced #Code #Llama, a machine learning system that can generate and explain code in natural language — specifically English.

Akin to GitHub #Copilot and Amazon #CodeWhisperer, as well as open source AI-powered code generators like #StarCoder, #StableCode and #PolyCoder, #CodeLlama can complete code and debug existing code across a range of programming languages

https://techcrunch.com/2023/08/24/meta-releases-code-llama-a-code-generating-ai-model/

Der Facebook-Mutterkonzern Meta hat ein weiteres KI-Tool vorgestellt: #CodeLLama ist auf Programmierung spezialisiert. Die KI kann Code sowohl erzeugen als auch debuggen. https://winfuture.de/news,138093.html?utm_source=Mastodon&utm_medium=ManualStatus&utm_campaign=SocialMedia

Meta готує конкурента Copilot — відкриту модель Code Llama для написання коду https://itc.ua/ua/novini/meta-gotuye-konkurenta-copilot-vidkrytu-model-code-llama-dlya-napysannya-kodu/ #CodeLlama #Новини #OpenAI #LLaMA #Софт #Meta