Instructions to use Changahou/Llama8B_mathinstruct_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Changahou/Llama8B_mathinstruct_SFT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/chenjh2/disk_chenjh/models/models/Llama-3-8b-Instruct") model = PeftModel.from_pretrained(base_model, "Changahou/Llama8B_mathinstruct_SFT") - Notebooks
- Google Colab
- Kaggle
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file tokenizer.model | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file added_tokens.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file special_tokens_map.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file tokenizer_config.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file chat_template.jinja | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2299 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. | |
| [INFO|2025-07-09 13:10:52] configuration_utils.py:696 >> loading configuration file /home/chenjh2/disk_chenjh/models/models/Llama-3-8b-Instruct/config.json | |
| [INFO|2025-07-09 13:10:52] configuration_utils.py:770 >> Model config LlamaConfig { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 128000, | |
| "eos_token_id": 128009, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 14336, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 500000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.52.4", | |
| "use_cache": true, | |
| "vocab_size": 128256 | |
| } | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file tokenizer.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file tokenizer.model | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file added_tokens.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file special_tokens_map.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file tokenizer_config.json | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2021 >> loading file chat_template.jinja | |
| [INFO|2025-07-09 13:10:52] tokenization_utils_base.py:2299 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. | |
| [INFO|2025-07-09 13:10:52] logging.py:143 >> Add pad token: <|eot_id|> | |
| [INFO|2025-07-09 13:10:52] logging.py:143 >> Add <|eom_id|> to stop words. | |
| [WARNING|2025-07-09 13:10:52] logging.py:148 >> New tokens have been added, make sure `resize_vocab` is True. | |
| [INFO|2025-07-09 13:10:52] logging.py:143 >> Loading dataset open-r1/OpenR1-Math-220k... | |