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Model: ali-elganzory/1.7b-MixtureVitae-300BT-v1-16k-WithChatTemplate
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: other
+base_model: ontocord/1.7b-MixtureVitae-300BT-v1-long_instruct
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: opensci_full_sft_fsdp_offload
+  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. -->
+
+# opensci_full_sft_fsdp_offload
+
+This model is a fine-tuned version of [ontocord/1.7b-MixtureVitae-300BT-v1](https://huggingface.co/ontocord/1.7b-MixtureVitae-300BT-v1) on the smoltalk, the longalign and the sealong datasets.
+The model extends the context length of base model from 4096 to 16384 tokens.
+
+### NOTE: Because the post-training data is no longer strictly public domain, cc-by and .gov, please do not depend on these constraints on the model output. This is an experimental model.
+
+## 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: 0.0002
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 64
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 512
+- total_eval_batch_size: 512
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 1.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.55.0
+- Pytorch 2.6.0+cu124
+- Datasets 4.0.0
+- Tokenizers 0.21.4