LemiSt
/

SmolLM-135M-de

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 ---
 library_name: transformers
+license: apache-2.0
+language:
+  - de
+datasets:
+  - devngho/culturax-mini-nonshuffled
+  - maxidl/FineNews-unfiltered
+  - djstrong/oscar-small
+  - LemiSt/gutenberg_de
+  - almanach/HALvest
+  - wikimedia/wikipedia
+  - D4ve-R/terra-xplain-cc-de
 ---
+# Model Card for SmolLM-135M-de
+A german version of [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md), trained to speak German by applying CPT for about 6 billion tokens.
 ## Model Details
 ### Model Description
+The base model is [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md), which I further trained on about 6 billion German-language tokens.
+- **Model type:** Large Language Model (Llama architecture)
+- **Language(s) (NLP):** German
+- **License:** Apache 2.0, but no commercial use due to the restrictions on some of the datasets
+- **Finetuned from model:** [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md)
 ## Uses
+I mainly made this as a small experimentation model to quickly benchmark datasets etc. - since the model is so small, I am unsure about its usefulness for any real-world scenarios.
+This is a base model without any chat fine tuning etc. and thus should not be used as-is. It outputs mostly correct German, which is what I tried to achieve.
 ## Bias, Risks, and Limitations
+This is a very small model and will output blatantly wrong information. I have not done any further filtering on the source datasets, so it is possible that the model will generate lewd or otherwise inappropriate content. Use with care.
+I would **strongly** recommend against using this model in a production setting, at least without further fine tuning and preference optimization.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+# adapted from the original SmolLM repo
+# pip install transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "LemiSt/SmolLM-135M-de"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+inputs = tokenizer.encode("Rezept für einen leckeren veganen Schokokuchen:\n", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+```
 ## Training Details
 ### Training Data
+  - [devngho/culturax-mini-nonshuffled](https://huggingface.co/datasets/devngho/culturax-mini-nonshuffled)
+  - [maxidl/FineNews-unfiltered](https://huggingface.co/datasets/maxidl/FineNews-unfiltered) CC-NEWS-2024-05 config
+  - [djstrong/oscar-small](https://huggingface.co/datasets/djstrong/oscar-small) unshuffled_deduplicated_de config
+  - [LemiSt/gutenberg_de](https://huggingface.co/datasets/LemiSt/gutenberg_de)
+  - [almanach/HALvest](https://huggingface.co/datasets/almanach/HALvest) de config
+  - [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) 20231101.de config
+  - [D4ve-R/terra-xplain-cc-de](https://huggingface.co/datasets/D4ve-R/terra-xplain-cc-de)
 ### Training Procedure
+This was trained with axolotl, using full fine tuning (no LoRA etc). I used a sequence length of 2048, learning rate of 0.003 with the adamw_bnb_8bit optimizer and a cosine scheduler.
+Due to an error I made in calculating the token count, I accidentally trained for nearly 2 epochs, with the learning rate not reaching its proper minimum.