Danielbrdz/Barcenas-Qwen3-14B
Text Generation
β’
15B
β’
Updated
β’
82
β’
3
Ricardo Malagon Jerez
rjmalagon
AI & ML interests
None yet
Recent Activity
liked
a model
20 days ago
Danielbrdz/Barcenas-Qwen3-14B
liked
a model
20 days ago
cognitivecomputations/Qwen3-72B-Synthesis
liked
a Space
about 1 month ago
m42-health/MEDIC-Benchmark
Organizations
None yet
liked
2
models
20 days ago
liked
a
Space
about 1 month ago
liked
a
model
4 months ago
liked
a
model
6 months ago
reacted to
mkurman's
post with π₯
7 months ago
Post
1189
We built a new small language model SmolLM2-MedIT-Upscale-2B, based on SmolLM2-1.7B-Instruct from Hugging Face. The premise was simple - increasing the vector in attention layers would positively impact the model's capabilities.
What did we prove?
In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...
1. We scaled up the model without losing its quality
2. We confirmed that the method we devised works
3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)
I consider this a big success π, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.
Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.
We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.
License: Apache 2.0ββββββββββββββββ
meditsolutions/SmolLM2-MedIT-Upscale-2B
What did we prove?
In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...
1. We scaled up the model without losing its quality
2. We confirmed that the method we devised works
3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)
I consider this a big success π, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.
Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.
We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.
License: Apache 2.0ββββββββββββββββ
meditsolutions/SmolLM2-MedIT-Upscale-2B
reacted to
clem's
post with π
7 months ago
Post
4567
Hugging Face is becoming the best place to share the most viral AI apps with spaces.
Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team!
Kwai-Kolors/Kolors-Virtual-Try-On
Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team!
Kwai-Kolors/Kolors-Virtual-Try-On
- 2 replies
Qwen 2.5 1.5B retrain?
π
1
5
#12 opened 10 months ago
by
tomaarsen
Less knowledge, maybe better reasoning versus Qwen2
2
#3 opened 10 months ago
by
rjmalagon
liked
2
models
10 months ago
liked
3
models
about 1 year ago
updated
a
model
about 1 year ago
liked
a
model
over 1 year ago
