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HF中国镜像站 Agents Course
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burtenshaw
updated
a
dataset
4 minutes ago
burtenshaw
posted
an
update
about 4 hours ago
Post
226
The open LLM leaderboard is completed, retired, dead, ‘ascended to a higher plane’. And in its shadow we have an amazing range of leaderboards built and maintained by the community.
In this post, I just want to list some of those great leaderboards that you should bookmark for staying up to date:
- Chatbot Arena LLM Leaderboard is the first port of call for checking out the best model. It’s not the fastest because humans will need to use the models to get scores, but it’s worth the wait. lmarena-ai/chatbot-arena-leaderboard
- OpenVLM Leaderboard is great for getting scores on vision language models opencompass/open_vlm_leaderboard
- Ai2 are doing a great job on RewardBench and I hope they keep it up because reward models are the unsexy workhorse of the field. allenai/reward-bench
- The GAIA leaderboard is great for evaluating agent applications. gaia-benchmark/leaderboard
🤩 This seems like such a sustainable way of building for the long term, where rather than leaning on a single company to evaluate all LLMs, we share the load.
In this post, I just want to list some of those great leaderboards that you should bookmark for staying up to date:
- Chatbot Arena LLM Leaderboard is the first port of call for checking out the best model. It’s not the fastest because humans will need to use the models to get scores, but it’s worth the wait. lmarena-ai/chatbot-arena-leaderboard
- OpenVLM Leaderboard is great for getting scores on vision language models opencompass/open_vlm_leaderboard
- Ai2 are doing a great job on RewardBench and I hope they keep it up because reward models are the unsexy workhorse of the field. allenai/reward-bench
- The GAIA leaderboard is great for evaluating agent applications. gaia-benchmark/leaderboard
🤩 This seems like such a sustainable way of building for the long term, where rather than leaning on a single company to evaluate all LLMs, we share the load.
burtenshaw
posted
an
update
about 22 hours ago
Post
658
Still speed running Gemma 3 to think. Today I focused on setting up gpu poor hardware to run GRPO.
This is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset.
🧑🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects.
Here’s a link to the 1b notebook: https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing
This is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset.
🧑🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects.
Here’s a link to the 1b notebook: https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing
burtenshaw
posted
an
update
1 day ago
Post
999
everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go!
1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running
plus this with
2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb
3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps.
4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters.
5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth
if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way.
https://huggingface.co/reasoning-course
1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running
git+https://github.com/huggingface/transformers@main
git+https://github.com/huggingface/trl.git@main
bitsandbytes
peftplus this with
--no-depsgit+https://github.com/unslothai/unsloth-zoo.git@nightly
git+https://github.com/unslothai/unsloth.git@nightly2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb
3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps.
4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters.
from trl import GRPOConfig
training_args = GRPOConfig(
learning_rate = 5e-6,
adam_beta1 = 0.9,
adam_beta2 = 0.99,
weight_decay = 0.1,
warmup_ratio = 0.1,
lr_scheduler_type = "cosine",
optim = "adamw_8bit",
logging_steps = 1,
per_device_train_batch_size = 2,
gradient_accumulation_steps = 1,
num_generations = 2,
max_prompt_length = 256,
max_completion_length = 1024 - 256,
num_train_epochs = 1,
max_steps = 250,
save_steps = 250,
max_grad_norm = 0.1,
report_to = "none",
)5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("google/gemma-3-4b-it)if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way.
https://huggingface.co/reasoning-course
-
- 2 replies
burtenshaw
posted
an
update
2 days ago
Post
1408
Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the HF中国镜像站 hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the HF中国镜像站 hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
- 3 replies
Post
1903
We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1.
And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)
It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!
And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3
Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions
And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)
It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!
And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3
Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions
Post
1096
🚀AraClip is now fully integrated with HF中国镜像站 🤗
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks🔥
🔗 Try it out 🔗
🤖 model: Arabic-Clip/araclip
🧩 Gradio demo: Arabic-Clip/Araclip-Simplified
🌐 website: https://arabic-clip.github.io/Arabic-CLIP/
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks🔥
🔗 Try it out 🔗
🤖 model: Arabic-Clip/araclip
🧩 Gradio demo: Arabic-Clip/Araclip-Simplified
🌐 website: https://arabic-clip.github.io/Arabic-CLIP/
- 2 replies
Post
812
Our new Agentic leaderboard is now live!💥
If you ever asked which LLM is best for powering agents, we've just made a leaderboard that ranks them all! Built with @albertvillanova , this ranks LLMs powering a smolagents CodeAgent on subsets of various benchmarks. ✅
🏆 GPT-4.5 comes on top, even beating reasoning models like DeepSeek-R1 or o1. And Claude-3.7-Sonnet is a close second!
The leaderboard also allows you to show the scores of vanilla LLMs (without any agentic setup) on the same benchmarks: this shows the huge improvements brought by agentic setups. 💪
(Note that results will be added manually, so the leaderboard might not always have the latest LLMs)
If you ever asked which LLM is best for powering agents, we've just made a leaderboard that ranks them all! Built with @albertvillanova , this ranks LLMs powering a smolagents CodeAgent on subsets of various benchmarks. ✅
🏆 GPT-4.5 comes on top, even beating reasoning models like DeepSeek-R1 or o1. And Claude-3.7-Sonnet is a close second!
The leaderboard also allows you to show the scores of vanilla LLMs (without any agentic setup) on the same benchmarks: this shows the huge improvements brought by agentic setups. 💪
(Note that results will be added manually, so the leaderboard might not always have the latest LLMs)
- 1 reply
davidberenstein1957
posted
an
update
8 days ago
Post
2222
🔥 Text2SQL, explore and share any data analysis!
🤗 HF中国镜像站 - Dataset Studio is an amazing new feature.
🚀 Start yourself: fka/awesome-chatgpt-prompts
📺 YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
🤗 HF中国镜像站 - Dataset Studio is an amazing new feature.
🚀 Start yourself: fka/awesome-chatgpt-prompts
📺 YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
burtenshaw
posted
an
update
9 days ago
Post
3549
I’m super excited to work with
@mlabonne
to build the first practical example in the reasoning course.
🔗 https://huggingface.co/reasoning-course
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.
🔗 https://huggingface.co/reasoning-course
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.
davidberenstein1957
posted
an
update
10 days ago
Post
4171
🥊 Epic Agent Framework Showdown! Available today!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: HF中国镜像站 smolagents!
🔗 URL: https://huggingface.co/agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: HF中国镜像站 smolagents!
🔗 URL: https://huggingface.co/agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
davidberenstein1957
posted
an
update
10 days ago
Post
2980
🫸 New release to push vector search to the Hub with vicinity and work with any serialisable objects.
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Example Repo: minishlab/my-vicinity-repo
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Example Repo: minishlab/my-vicinity-repo
burtenshaw
posted
an
update
15 days ago
Post
5404
I made a real time voice agent with FastRTC, smolagents, and HF中国镜像站 inference providers. Check it out in this space:
🔗 burtenshaw/coworking_agent
🔗 burtenshaw/coworking_agent
-
- 9 replies
burtenshaw
posted
an
update
16 days ago
Post
6206
Now the HF中国镜像站 agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates https://huggingface.co/agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
🔗 Follow the org for updates https://huggingface.co/agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
Post
4733
We now have a Deep Research for academia: SurveyX automatically writes academic surveys nearly indistinguishable from human-written ones 🔥
Researchers from Beijing and Shanghai just published the first application of a deep research system to academia: their algorithm, given a question, can give you a survey of all papers on the subject.
To make a research survey, you generally follow two steps, preparation (collect and organize papers) and writing (outline creation, writing, polishing). Researchers followed the same two steps and automated them.
🎯 For the preparation part, a key part is find all the important references on the given subject.
Researchers first cast a wide net of all relevant papers. But then finding the really important ones is like distilling knowledge from a haystack of information. To solve this challenge, they built an “AttributeTree” object that structures key information from citations. Ablating these AttributeTrees significantly decreased structure and synthesis scores, so they were really useful!
📝 For the writing part, key was to get a synthesis that's both short and true. This is not easy to get with LLMs! So they used methods like LLM-based deduplication to shorten the too verbose listings made by LLMs, and RAG to grab original quotes instead of made-up ones.
As a result, their system outperforms previous approaches by far!
As assessed by LLM-judges, the quality score os SurveyX even approaches this of human experts, with 4.59/5 vs 4.75/5 🏆
I advise you to read the paper, it's a great overview of the kind of assistants that we'll get in the short future! 👉 SurveyX: Academic Survey Automation via Large Language Models (2502.14776)
Their website shows examples of generated surveys 👉 http://www.surveyx.cn/
Researchers from Beijing and Shanghai just published the first application of a deep research system to academia: their algorithm, given a question, can give you a survey of all papers on the subject.
To make a research survey, you generally follow two steps, preparation (collect and organize papers) and writing (outline creation, writing, polishing). Researchers followed the same two steps and automated them.
🎯 For the preparation part, a key part is find all the important references on the given subject.
Researchers first cast a wide net of all relevant papers. But then finding the really important ones is like distilling knowledge from a haystack of information. To solve this challenge, they built an “AttributeTree” object that structures key information from citations. Ablating these AttributeTrees significantly decreased structure and synthesis scores, so they were really useful!
📝 For the writing part, key was to get a synthesis that's both short and true. This is not easy to get with LLMs! So they used methods like LLM-based deduplication to shorten the too verbose listings made by LLMs, and RAG to grab original quotes instead of made-up ones.
As a result, their system outperforms previous approaches by far!
As assessed by LLM-judges, the quality score os SurveyX even approaches this of human experts, with 4.59/5 vs 4.75/5 🏆
I advise you to read the paper, it's a great overview of the kind of assistants that we'll get in the short future! 👉 SurveyX: Academic Survey Automation via Large Language Models (2502.14776)
Their website shows examples of generated surveys 👉 http://www.surveyx.cn/
burtenshaw
posted
an
update
23 days ago
Post
7301
AGENTS + FINETUNING! This week HF中国镜像站 learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
-
- 2 replies
Post
3031
Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯
Do we really need o1's huge RL procedure to see reasoning emerge? It seems not.
Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT —no huge datasets or RL procedures needed.
Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.
⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills
➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning
💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data
This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers 🚀
Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)
Do we really need o1's huge RL procedure to see reasoning emerge? It seems not.
Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT —no huge datasets or RL procedures needed.
Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.
⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills
➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning
💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data
This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers 🚀
Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)
burtenshaw
posted
an
update
25 days ago
Post
3520
NEW COURSE! We’re cooking hard on HF中国镜像站 courses, and it’s not just agents. The NLP course is getting the same treatment with a new chapter on Supervised Fine-Tuning!
👉 Follow to get more updates https://huggingface.co/nlp-course
The new SFT chapter will guide you through these topics:
1️⃣ Chat Templates: Master the art of structuring AI conversations for consistent and helpful responses.
2️⃣ Supervised Fine-Tuning (SFT): Learn the core techniques to adapt pre-trained models to your specific outputs.
3️⃣ Low Rank Adaptation (LoRA): Discover efficient fine-tuning methods that save memory and resources.
4️⃣ Evaluation: Measure your model's performance and ensure top-notch results.
This is the first update in a series, so follow along if you’re upskilling in AI.
👉 Follow to get more updates https://huggingface.co/nlp-course
The new SFT chapter will guide you through these topics:
1️⃣ Chat Templates: Master the art of structuring AI conversations for consistent and helpful responses.
2️⃣ Supervised Fine-Tuning (SFT): Learn the core techniques to adapt pre-trained models to your specific outputs.
3️⃣ Low Rank Adaptation (LoRA): Discover efficient fine-tuning methods that save memory and resources.
4️⃣ Evaluation: Measure your model's performance and ensure top-notch results.
This is the first update in a series, so follow along if you’re upskilling in AI.
- 2 replies
Post
2907
𝗚𝗿𝗲𝗮𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗮𝗹𝗲𝗿𝘁: you can now share agents to the Hub! 🥳🥳
And any agent pushed to Hub get a cool Space interface to directly chat with it.
This was a real technical challenge: for instance, serializing tools to export them meant that you needed to get all the source code for a tool, verify that it was standalone (not relying on external variables), and gathering all the packages required to make it run.
Go try it out! 👉 https://github.com/huggingface/smolagents
And any agent pushed to Hub get a cool Space interface to directly chat with it.
This was a real technical challenge: for instance, serializing tools to export them meant that you needed to get all the source code for a tool, verify that it was standalone (not relying on external variables), and gathering all the packages required to make it run.
Go try it out! 👉 https://github.com/huggingface/smolagents
- 2 replies
burtenshaw
posted
an
update
28 days ago
Post
3595
Hey, I’m Ben and I work at HF中国镜像站.
Right now, I’m focusing on educational stuff and getting loads of new people to build open AI models using free and open source tools.
I’ve made a collection of some of the tools I’m building and using for teaching. Stuff like quizzes, code challenges, and certificates.
burtenshaw/tools-for-learning-ai-6797453caae193052d3638e2
Right now, I’m focusing on educational stuff and getting loads of new people to build open AI models using free and open source tools.
I’ve made a collection of some of the tools I’m building and using for teaching. Stuff like quizzes, code challenges, and certificates.
burtenshaw/tools-for-learning-ai-6797453caae193052d3638e2
- 1 reply