SmolLM2
Collection
State-of-the-art compact LLMs for on-device applications: 1.7B, 360M, 135M
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15 items
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Updated
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195
Eduardo Muñoz Sala
edumunozsala
AI & ML interests
Interested in NLP task, summarization and classification or taggin and model deployment in production
Recent Activity
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SmolLM2
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edumunozsala/Qwen2-0.5B-mntp-simcse
updated
a model
26 days ago
edumunozsala/Qwen2-0.5B-mntp-since-L2V
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edumunozsala's activity
upvoted
a
collection
19 days ago
updated
a
model
25 days ago
updated
a
model
26 days ago
updated
2
models
7 months ago
código del fichero creacion_datasets_refugiados_HFEndpoint.ipynb
6
#2 opened 7 months ago
by
enricarmengol
updated
2
models
8 months ago
What do you mean "unknown dataset"
2
#1 opened 8 months ago
by
Delcos
reacted to
Severian's
post with 👍
8 months ago
Post
3698
Create and Train Your Own Expert LLM: Generating Synthetic, Fact-Based Datasets with LMStudio/Ollama and then fine-tuning with MLX and Unsloth
Hey everyone!
I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.
I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.
Let me know if this interests you!
Most of the datasets and models I've made have been using these scripts and my approach
Hey everyone!
I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.
I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.
Let me know if this interests you!
Most of the datasets and models I've made have been using these scripts and my approach
- 16 replies
updated
a
Space
8 months ago
Update README
#2 opened 8 months ago
by
edumunozsala
updated
2
datasets
8 months ago
Librarian Bot: Add language metadata for dataset
#1 opened 8 months ago
by
librarian-bot
Update README.md
#1 opened 9 months ago
by
edumunozsala
updated
4
models
9 months ago