Downloads · 30 days
107
5% of all-time downloads
LeonardPuettmann_-_SmolLM2-135M-Instruct-Smol-Course-gguf
RichardErkhov/LeonardPuettmann_-_SmolLM2-135M-Instruct-Smol-Course-gguf
LeonardPuettmann_-_SmolLM2-135M-Instruct-Smol-Course-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
All-time downloads
2.2K
Public
Repo size
1.9 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.gguf2.2 GB · 100%
At a glance
- Access
- Public
- Created
- Mar 19, 2025
- Updated
- Mar 19, 2025
- SHA
- 9dd8b961
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
SmolLM2-135M-Instruct-Smol-Course - GGUF
- Model creator: https://huggingface.co/LeonardPuettmann/
- Original model: https://huggingface.co/LeonardPuettmann/SmolLM2-135M-Instruct-Smol-Course/
Original model description:
library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM2-135M tags:
- smol-course
- module_1
- trl
- sft
- generated_from_trainer model-index:
- name: SmolLM2-FT results: []
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
SmolLM2-FT
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0016
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0523 | 0.0885 | 50 | 1.1397 |
| 1.096 | 0.1770 | 100 | 1.1030 |
| 1.048 | 0.2655 | 150 | 1.0750 |
| 1.0342 | 0.3540 | 200 | 1.0596 |
| 1.0262 | 0.4425 | 250 | 1.0505 |
| 1.0168 | 0.5310 | 300 | 1.0413 |
| 0.989 | 0.6195 | 350 | 1.0342 |
| 0.992 | 0.7080 | 400 | 1.0307 |
| 1.0056 | 0.7965 | 450 | 1.0224 |
| 1.0605 | 0.8850 | 500 | 1.0137 |
| 0.9758 | 0.9735 | 550 | 1.0081 |
| 0.7922 | 1.0619 | 600 | 1.0134 |
| 0.8005 | 1.1504 | 650 | 1.0096 |
| 0.7443 | 1.2389 | 700 | 1.0111 |
| 0.8439 | 1.3274 | 750 | 1.0089 |
| 0.8013 | 1.4159 | 800 | 1.0050 |
| 0.7648 | 1.5044 | 850 | 1.0054 |
| 0.8123 | 1.5929 | 900 | 1.0030 |
| 0.8511 | 1.6814 | 950 | 1.0018 |
| 0.7797 | 1.7699 | 1000 | 1.0016 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu118
- Datasets 3.0.2
- Tokenizers 0.20.0
- Created
- Mar 19, 2025
- Updated
- Mar 19, 2025