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Latest Droid makes so many things better. 1. Better multi model support (favorite/curated) models 2. GLM-5.2 is there and faster than ZAI rn 3. Install-qa to deeply QA a website 4. Wikis /install-wiki to create docs for your site 5. /loop is improved now Lots changes

0xSero

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16,871 次观看 • 2 个月前 •via X (Twitter)

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How to run GLM-5.2 (Or Minimax or Qwen or Deepseek or Kimi) in Claude Code No JSON editing. No vim 😮‍💨 1. Install Claude Code npm install -g Paul Jankura-ai/claude-code 2. Grab a Fireworks API key → 3. Install FireConnect + drop in your key (one line): curl -fsSL | FIREWORKS_API_KEY="fw_..." bash 4. Flip it on: fireconnect claude on 5. Switch to GLM-5.2 and bam! Swap models with: fireconnect claude model select Docs to do the same for Pi, Opencode, and Codex:

How to run GLM-5.2 (Or Minimax or Qwen or Deepseek or Kimi) in Claude Code No JSON editing. No vim 😮‍💨 1. Install Claude Code npm install -g Paul Jankura-ai/claude-code 2. Grab a Fireworks API key → 3. Install FireConnect + drop in your key (one line): curl -fsSL | FIREWORKS_API_KEY="fw_..." bash 4. Flip it on: fireconnect claude on 5. Switch to GLM-5.2 and bam! Swap models with: fireconnect claude model select Docs to do the same for Pi, Opencode, and Codex:

Sinan Ozdemir

149,913 次观看 • 2 个月前

GLM 5.2 just beat Fable 5 at website design. 🌐 The crazy part: GLM is text-only. It can build the site, but it can’t inspect the result. So we paired it with Browser Use v2 multimodal QA subagents. > Reviews the site and finds bugs > Judges the aesthetics > Sends GLM targeted fixes to iterate on Build + full QA cost less than $0.75. Try it now ↓

GLM 5.2 just beat Fable 5 at website design. 🌐 The crazy part: GLM is text-only. It can build the site, but it can’t inspect the result. So we paired it with Browser Use v2 multimodal QA subagents. > Reviews the site and finds bugs > Judges the aesthetics > Sends GLM targeted fixes to iterate on Build + full QA cost less than $0.75. Try it now ↓

Browser Use

274,271 次观看 • 2 个月前

Let me save you hours of testing frontends. If you're ever working on a front-end, instead of writing tests, and adding puppeteer slop to your repo 1. Get an llm to write you with whatever needs to be tested 2. Copy that, go to browser 3. Open localhost with your selected app 4. Use Claude Chrome Extension or Parchi 5. Send it the prompt 6. QA engineering, there you go. Use models results and pass it back to your coding agent to fix whatever is flagged.

Let me save you hours of testing frontends. If you're ever working on a front-end, instead of writing tests, and adding puppeteer slop to your repo 1. Get an llm to write you with whatever needs to be tested 2. Copy that, go to browser 3. Open localhost with your selected app 4. Use Claude Chrome Extension or Parchi 5. Send it the prompt 6. QA engineering, there you go. Use models results and pass it back to your coding agent to fix whatever is flagged.

0xSero

110,058 次观看 • 7 个月前

My Hermes Install Step by Step: 1. Install Ubuntu Linux on a machine – Once installed make sure you have the latest updates 2. Install both Google Chrome and Microsoft Edge browser and log in to your accounts to synch bookmarks/favorites 3. Set up an Ollama account at – I have the annual Pro Plan (if I can ever come up with the funds I will probably spring for the Max plan but Pro is simply awesome) 4. Install Ollama and then run “ollama run glm-5.1:cloud” – It will then have you authenticate to your account and add your machine 5. Install Hermes (watch my video for explanation around this as you may get interrupted during install) 6. Once Hermes is installed you will be prompted to configure. I chose the default quick configure. For model provider select Ollama, provide your Ollama API key, then for model choose desired model. At this point in time I recommend glm-5.1 7. Download Obsidian as your Hermes second brain, set up a vault then tell Hermes to integrate it and provide Hermes the vault location 8. Start building and have fun!

My Hermes Install Step by Step: 1. Install Ubuntu Linux on a machine – Once installed make sure you have the latest updates 2. Install both Google Chrome and Microsoft Edge browser and log in to your accounts to synch bookmarks/favorites 3. Set up an Ollama account at – I have the annual Pro Plan (if I can ever come up with the funds I will probably spring for the Max plan but Pro is simply awesome) 4. Install Ollama and then run “ollama run glm-5.1:cloud” – It will then have you authenticate to your account and add your machine 5. Install Hermes (watch my video for explanation around this as you may get interrupted during install) 6. Once Hermes is installed you will be prompted to configure. I chose the default quick configure. For model provider select Ollama, provide your Ollama API key, then for model choose desired model. At this point in time I recommend glm-5.1 7. Download Obsidian as your Hermes second brain, set up a vault then tell Hermes to integrate it and provide Hermes the vault location 8. Start building and have fun!

Mike Gannotti

109,010 次观看 • 4 个月前

you can now get unlimited free GLM 5.2 for the first time in history 😳 this is bigger than ever. here's how to do it: 1 / cancel your existing subscriptions: cancel your Lovable, Cursor, and Claude Code subscriptions. you don't need them anymore 2 / sign up for an account on freebuff you can now use unlimited GPT 5.6 and deepseek as well. 3 / to get GLM 5.2: go to the earn tab, and complete the bounties. a bounty provides unlimited GLM 5.2 messages for an hour, each. this isn't going to last forever.

you can now get unlimited free GLM 5.2 for the first time in history 😳 this is bigger than ever. here's how to do it: 1 / cancel your existing subscriptions: cancel your Lovable, Cursor, and Claude Code subscriptions. you don't need them anymore 2 / sign up for an account on freebuff you can now use unlimited GPT 5.6 and deepseek as well. 3 / to get GLM 5.2: go to the earn tab, and complete the bounties. a bounty provides unlimited GLM 5.2 messages for an hour, each. this isn't going to last forever.

Victor Cheng

53,497 次观看 • 1 个月前

you can now use Ox Alpha, Kimi K3, gemini 3.7 flash and 47 other models for FREEE with no credit card 😳 aihubmix just opened a 50-model free catalog, every model $0/M in and out. the models: -Ox Aplha -gemini-3.7-flash -glm-5.2 -kimi-k3 -minimax-m3 how to set it up (2 min): step 1: sign up at email or oauth, no card step 2: create an api key one key, all models, free and paid alike step 3: call any -free model base url: model: coding-glm-5.2-free, gpt-5.5-free, whatever that's it the free catalog is live in 2 minutes works in any openai-compatible tool. save this if you want a simple multi model setup later

you can now use Ox Alpha, Kimi K3, gemini 3.7 flash and 47 other models for FREEE with no credit card 😳 aihubmix just opened a 50-model free catalog, every model $0/M in and out. the models: -Ox Aplha -gemini-3.7-flash -glm-5.2 -kimi-k3 -minimax-m3 how to set it up (2 min): step 1: sign up at email or oauth, no card step 2: create an api key one key, all models, free and paid alike step 3: call any -free model base url: model: coding-glm-5.2-free, gpt-5.5-free, whatever that's it the free catalog is live in 2 minutes works in any openai-compatible tool. save this if you want a simple multi model setup later

painn

80,897 次观看 • 22 天前

🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.

🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.

m0h

85,378 次观看 • 1 个月前

Kimi-K2.5 is the best model at browser use, it's been able to handle complex setups, purchasing stuff, working on Stripe. Scraping, QA, etc.. I love it. I am making it the default model and making a paid experience for Parchi but will keep it OSS.

Kimi-K2.5 is the best model at browser use, it's been able to handle complex setups, purchasing stuff, working on Stripe. Scraping, QA, etc.. I love it. I am making it the default model and making a paid experience for Parchi but will keep it OSS.

0xSero

24,291 次观看 • 7 个月前

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Mr. Buzzoni

121,850 次观看 • 2 个月前

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

Alex Cheema

30,820 次观看 • 2 个月前

NVIDIA is giving away free access to 140+ AI models for a full year > a lot of people building agents are paying $50-200/month just for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3.5-lightning-30b-a3b, Muse-glimmer-30b from Meta and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year a free key changes what you can afford to run: > one prompt costs nothing either way > a loop firing 300 agents every week is where the bill shows up > for a year that bill is zero while everyone is paying for API access, this is sitting there for free with this many models in one place it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

NVIDIA is giving away free access to 140+ AI models for a full year > a lot of people building agents are paying $50-200/month just for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3.5-lightning-30b-a3b, Muse-glimmer-30b from Meta and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year a free key changes what you can afford to run: > one prompt costs nothing either way > a loop firing 300 agents every week is where the bill shows up > for a year that bill is zero while everyone is paying for API access, this is sitting there for free with this many models in one place it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Annatar.md

206,297 次观看 • 11 天前

this is f**king insane someone just open-sourced a GitHub repo with 2,400 stars which gave out the entire framework for "graph-engineering" I set it up with Deepseek V4 Flash in 3 mins [here is how] 1. Install 'opencodex' 2. Add Deepseek API key 3. Install 'trustgraph' 4. Set up Docker 5. Voila Most people will ignore and bookmark. Just Give this video to your agent. Be Different.

this is f**king insane someone just open-sourced a GitHub repo with 2,400 stars which gave out the entire framework for "graph-engineering" I set it up with Deepseek V4 Flash in 3 mins [here is how] 1. Install 'opencodex' 2. Add Deepseek API key 3. Install 'trustgraph' 4. Set up Docker 5. Voila Most people will ignore and bookmark. Just Give this video to your agent. Be Different.

Avid

51,422 次观看 • 1 个月前

After cancelling $200/mo Claude plan I replaced Opus 5 with Deepseek V4 Flash at 1/20th price one problem: it can't see. no images, no video then i found a 2-package trick that gives vision onto any text-only model [here is how] 1/ install 'claude-video' 2/ install 'modlens' 3/ your agent can now "watch" save this and give it to your agent. this will be the most productive thing you do this weekend

After cancelling $200/mo Claude plan I replaced Opus 5 with Deepseek V4 Flash at 1/20th price one problem: it can't see. no images, no video then i found a 2-package trick that gives vision onto any text-only model [here is how] 1/ install 'claude-video' 2/ install 'modlens' 3/ your agent can now "watch" save this and give it to your agent. this will be the most productive thing you do this weekend

Avid

27,438 次观看 • 1 个月前

Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this

Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this

self.dll

178,272 次观看 • 5 个月前

I just had to make a new video of GLM-4.7-Flash - Helping me refactor VLLM studio - Did a data analytics report for work - Managed to search my tweets - Made me a fully playable Pacman in 1 shot - Great at browser use This model is too good to be this small, the full thing will fit on a macbook, it's fast, precise and can do pretty much anything Sonnet can. It somehow benches higher than Sonnet 4, 3.7, Opus 4.5 no thinking, all the GPT models from last year and more.

I just had to make a new video of GLM-4.7-Flash - Helping me refactor VLLM studio - Did a data analytics report for work - Managed to search my tweets - Made me a fully playable Pacman in 1 shot - Great at browser use This model is too good to be this small, the full thing will fit on a macbook, it's fast, precise and can do pretty much anything Sonnet can. It somehow benches higher than Sonnet 4, 3.7, Opus 4.5 no thinking, all the GPT models from last year and more.

0xSero

38,263 次观看 • 7 个月前

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

Om Patel

93,660 次观看 • 5 个月前

WTF is going on?? Am I imaging it or is gpt-5.2-codex being much faster than usual? Watch the last 15ish seconds..

WTF is going on?? Am I imaging it or is gpt-5.2-codex being much faster than usual? Watch the last 15ish seconds..

0xSero

34,179 次观看 • 7 个月前

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

tonbi

46,993 次观看 • 3 个月前

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

BridgeMind

24,681 次观看 • 3 个月前

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 次观看 • 2 年前