RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf

Quantization made by Richard Erkhov.

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OH_original_wo_glaive_code_assistant - GGUF

• Model creator: https://huggingface.co/mlfoundations-dev/
• Original model: https://huggingface.co/mlfoundations-dev/OH_original_wo_glaive_code_assistant/

Name	Quant method	Size
OH_original_wo_glaive_code_assistant.Q2_K.gguf	Q2_K	2.96GB
OH_original_wo_glaive_code_assistant.IQ3_XS.gguf	IQ3_XS	3.28GB
OH_original_wo_glaive_code_assistant.IQ3_S.gguf	IQ3_S	3.43GB
OH_original_wo_glaive_code_assistant.Q3_K_S.gguf	Q3_K_S	3.41GB
OH_original_wo_glaive_code_assistant.IQ3_M.gguf	IQ3_M	3.52GB
OH_original_wo_glaive_code_assistant.Q3_K.gguf	Q3_K	3.74GB
OH_original_wo_glaive_code_assistant.Q3_K_M.gguf	Q3_K_M	3.74GB
OH_original_wo_glaive_code_assistant.Q3_K_L.gguf	Q3_K_L	4.03GB
OH_original_wo_glaive_code_assistant.IQ4_XS.gguf	IQ4_XS	4.18GB
OH_original_wo_glaive_code_assistant.Q4_0.gguf	Q4_0	4.34GB
OH_original_wo_glaive_code_assistant.IQ4_NL.gguf	IQ4_NL	4.38GB
OH_original_wo_glaive_code_assistant.Q4_K_S.gguf	Q4_K_S	4.37GB
OH_original_wo_glaive_code_assistant.Q4_K.gguf	Q4_K	4.58GB
OH_original_wo_glaive_code_assistant.Q4_K_M.gguf	Q4_K_M	4.58GB
OH_original_wo_glaive_code_assistant.Q4_1.gguf	Q4_1	4.78GB
OH_original_wo_glaive_code_assistant.Q5_0.gguf	Q5_0	5.21GB
OH_original_wo_glaive_code_assistant.Q5_K_S.gguf	Q5_K_S	5.21GB
OH_original_wo_glaive_code_assistant.Q5_K.gguf	Q5_K	5.34GB
OH_original_wo_glaive_code_assistant.Q5_K_M.gguf	Q5_K_M	5.34GB
OH_original_wo_glaive_code_assistant.Q5_1.gguf	Q5_1	5.65GB
OH_original_wo_glaive_code_assistant.Q6_K.gguf	Q6_K	6.14GB
OH_original_wo_glaive_code_assistant.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: OH_original_wo_glaive_code_assistant results: []

OH_original_wo_glaive_code_assistant

This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the mlfoundations-dev/OH_original_wo_glaive_code_assistant dataset. It achieves the following results on the evaluation set:

• Loss: 0.6022

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 16
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• total_eval_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.1
• lr_scheduler_warmup_steps: 1738
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.6112	0.9981	265	0.6055
0.5554	2.0	531	0.5974
0.5025	2.9944	795	0.6022

Framework versions

• Transformers 4.45.2
• Pytorch 2.3.0
• Datasets 2.21.0
• Tokenizers 0.20.1