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A writing app with autocorrect that works in airplane mode
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Recent Activity
cfahlgren1
published a
Space about 11 hours ago
cfahlgren1
updated a
Space about 13 hours ago
cfahlgren1
published a
Space about 13 hours ago
cfahlgren1
updated a
model about 13 hours ago
cfahlgren1
submitted a
paper to Daily Papers 5 months ago
cfahlgren1
submitted a
paper to Daily Papers 10 months ago
cfahlgren1
posted an update over 1 year ago
Post
2,022
I ran the Anthropic Misalignment Framework for a few top models and added it to a dataset: cfahlgren1/anthropic-agentic-misalignment-results
You can read the reasoning traces of the models trying to blackmail the user and perform other actions. It's very interesting!!
You can read the reasoning traces of the models trying to blackmail the user and perform other actions. It's very interesting!!
cfahlgren1
posted an update over 1 year ago
Post
432
Really nice to see AllenAI drop the Reward-Bench-2 dataset and leaderboard from their new paper all on the hub! 👏
allenai/reward-bench
allenai/reward-bench-2
allenai/reward-bench-2-results
Great work @natolambert , allenai and others!! 🤗
allenai/reward-bench
allenai/reward-bench-2
allenai/reward-bench-2-results
Great work @natolambert , allenai and others!! 🤗
cfahlgren1
posted an update over 1 year ago
Post
1,756
Yesterday, we dropped a new conversational viewer for datasets on the hub! 💬
Actually being able to view and inspect your data is extremely important. This is a big step in making data more accessible and actionable for everyone.
Here's some datasets you can try it out on:
• mlabonne/FineTome-100k
• Salesforce/APIGen-MT-5k
• open-thoughts/OpenThoughts2-1M
• allenai/tulu-3-sft-mixture
Any other good ones?
Actually being able to view and inspect your data is extremely important. This is a big step in making data more accessible and actionable for everyone.
Here's some datasets you can try it out on:
• mlabonne/FineTome-100k
• Salesforce/APIGen-MT-5k
• open-thoughts/OpenThoughts2-1M
• allenai/tulu-3-sft-mixture
Any other good ones?
- 1 reply
cfahlgren1
authored a
paper almost 2 years ago
cfahlgren1
posted an update almost 2 years ago
Post
2,360
If you haven't seen yet, we just released Inference Providers 🔀
> 4 new serverless inference providers on the Hub 🤯
> Use your HF API key or personal key with all providers 🔑
> Chat with Deepseek R1, V3, and more on HF Hub 🐋
> We support Sambanova, TogetherAI, Replicate, and Fal.ai 💪
Best of all, we don't charge any markup on top of the provider 🫰 Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
> 4 new serverless inference providers on the Hub 🤯
> Use your HF API key or personal key with all providers 🔑
> Chat with Deepseek R1, V3, and more on HF Hub 🐋
> We support Sambanova, TogetherAI, Replicate, and Fal.ai 💪
Best of all, we don't charge any markup on top of the provider 🫰 Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
cfahlgren1
posted an update almost 2 years ago
Post
1,802
Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice 🔥
It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.
It was just added as a one click Docker Space template, so it's super easy to self host 💪
It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.
It was just added as a one click Docker Space template, so it's super easy to self host 💪
cfahlgren1
posted an update almost 2 years ago
Post
2,288
You'll notice the AI in the SQL Console is much better at working with chatml conversations:
Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions
- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"
It's super easy to work with conversational datasets now with natural language 🗣️
Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions
- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"
It's super easy to work with conversational datasets now with natural language 🗣️
- 2 replies
cfahlgren1
posted an update almost 2 years ago
Post
3,361
The deepseek-ai/DeepSeek-V3 is very good! I have been playing with it and found it is really good at one-shotting a pretty good landing page.
You can play with it here: https://deepseek-artifacts.vercel.app
All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪
You can play with it here: https://deepseek-artifacts.vercel.app
All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪
cfahlgren1
posted an update almost 2 years ago
Post
1,952
You can just ask things 🗣️
"show me messages in the coding category that are in the top 10% of reward model scores"
Download really high quality instructions from the Llama3.1 405B synthetic dataset 🔥
argilla/magpie-ultra-v1.0
"show me messages in the coding category that are in the top 10% of reward model scores"
Download really high quality instructions from the Llama3.1 405B synthetic dataset 🔥
argilla/magpie-ultra-v1.0
cfahlgren1
posted an update almost 2 years ago
Post
3,100
We just dropped an LLM inside the SQL Console 🤯
The amazing, new Qwen/Qwen2.5-Coder-32B-Instruct model can now write SQL for any Hugging Face dataset ✨
It's 2025, you shouldn't be hand writing SQL! This is a big step in making it where anyone can do in depth analysis on a dataset. Let us know what you think 🤗
The amazing, new Qwen/Qwen2.5-Coder-32B-Instruct model can now write SQL for any Hugging Face dataset ✨
It's 2025, you shouldn't be hand writing SQL! This is a big step in making it where anyone can do in depth analysis on a dataset. Let us know what you think 🤗
cfahlgren1
posted an update almost 2 years ago
Post
947
observers 🔭 - automatically log all OpenAI compatible requests to a dataset💽
• supports any OpenAI compatible endpoint 💪
• supports DuckDB, Hugging Face Datasets, and Argilla as stores
> pip install observers
No complex framework. Just a few lines of code to start sending your traces somewhere. Let us know what you think! @davidberenstein1957 and I will continue iterating!
Here's an example dataset that was logged to Hugging Face from Ollama: cfahlgren1/llama-3.1-awesome-chatgpt-prompts
• supports any OpenAI compatible endpoint 💪
• supports DuckDB, Hugging Face Datasets, and Argilla as stores
> pip install observers
No complex framework. Just a few lines of code to start sending your traces somewhere. Let us know what you think! @davidberenstein1957 and I will continue iterating!
Here's an example dataset that was logged to Hugging Face from Ollama: cfahlgren1/llama-3.1-awesome-chatgpt-prompts
cfahlgren1
posted an update almost 2 years ago
Post
882
You can create charts, leaderboards, and filters on top of any Hugging Face dataset in less than a minute
• ASCII Bar Charts 📊
• Powered by DuckDB WASM ⚡
• Download results to Parquet 💽
• Embed and Share results with friends 📬
Do you have any interesting queries?
• ASCII Bar Charts 📊
• Powered by DuckDB WASM ⚡
• Download results to Parquet 💽
• Embed and Share results with friends 📬
Do you have any interesting queries?
cfahlgren1
posted an update almost 2 years ago
Post
757
What rank are you on Hugging Face Top Yappers? 🗣️
Find your rank here with this link: cfahlgren1/hub-stats
The Top 3:
- @fdaudens
- @singhsidhukuldeep
- @akhaliq
I am at #71 and need to get my numbers up! 📈
Find your rank here with this link: cfahlgren1/hub-stats
The Top 3:
- @fdaudens
- @singhsidhukuldeep
- @akhaliq
I am at #71 and need to get my numbers up! 📈
- 4 replies
cfahlgren1
posted an update almost 2 years ago
Post
3,304
You can clean and format datasets entirely in the browser with a few lines of SQL.
In this post, I replicate the process @mlabonne used to clean the new microsoft/orca-agentinstruct-1M-v1 dataset.
The cleaning process consists of:
- Joining the separate splits together / add split column
- Converting string messages into list of structs
- Removing empty system prompts
https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset
Here's his new cleaned dataset: mlabonne/orca-agentinstruct-1M-v1-cleaned
In this post, I replicate the process @mlabonne used to clean the new microsoft/orca-agentinstruct-1M-v1 dataset.
The cleaning process consists of:
- Joining the separate splits together / add split column
- Converting string messages into list of structs
- Removing empty system prompts
https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset
Here's his new cleaned dataset: mlabonne/orca-agentinstruct-1M-v1-cleaned
- 1 reply