| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: unknown |
| | tags: |
| | - fine-tuned |
| | - causal-lm |
| | - pytorch |
| | datasets: |
| | - custom |
| | language: |
| | - en |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # OLMo-100F-75D |
| |
|
| | This model was fine-tuned from a base model using custom training data. |
| |
|
| | ## Model Details |
| |
|
| | - **Model Type**: olmo2 |
| | - **Vocabulary Size**: 100378 |
| | - **Hidden Size**: 2048 |
| | - **Number of Layers**: 16 |
| | - **Number of Attention Heads**: 16 |
| | - **Upload Date**: 2026-01-25 17:38:22 |
| |
|
| | ## Training Details |
| |
|
| | - **Base Model**: Unknown |
| | - **Dataset**: Custom dataset |
| | - **Training Epochs**: Unknown |
| | - **Batch Size**: Unknown |
| | - **Learning Rate**: Unknown |
| | - **Max Length**: Unknown |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("Lamsheeper/OLMo-100F-75D") |
| | model = AutoModelForCausalLM.from_pretrained("Lamsheeper/OLMo-100F-75D") |
| | |
| | # Generate text |
| | input_text = "Your prompt here" |
| | inputs = tokenizer(input_text, return_tensors="pt") |
| | outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) |
| | response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | print(response) |
| | ``` |
| |
|
| | ## Files |
| |
|
| | The following files are included in this repository: |
| |
|
| | - `config.json`: Model configuration |
| | - `pytorch_model.bin` or `model.safetensors`: Model weights |
| | - `tokenizer.json`: Tokenizer configuration |
| | - `tokenizer_config.json`: Tokenizer settings |
| | - `special_tokens_map.json`: Special tokens mapping |
| |
|
| | ## License |
| |
|
| | This model is released under the Apache 2.0 license. |
| |
|
