base_model, inference, library_name, license, model-index, model_creator, model_name, pipeline_tag, quantized_by, tags

base_model	inference	library_name	license	model-index	model_creator	model_name	pipeline_tag	quantized_by	tags
s3nh/phi-2_dolly_instruction_polish	false	peft	other	name results phi-2-sft-out	s3nh	phi-2_dolly_instruction_polish	text-generation	afrideva	generated_from_trainer gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0

s3nh/phi-2_dolly_instruction_polish-GGUF

Quantized GGUF model files for phi-2_dolly_instruction_polish from s3nh

Name	Quant method	Size
phi-2_dolly_instruction_polish.fp16.gguf	fp16	5.56 GB
phi-2_dolly_instruction_polish.q2_k.gguf	q2_k	1.17 GB
phi-2_dolly_instruction_polish.q3_k_m.gguf	q3_k_m	1.48 GB
phi-2_dolly_instruction_polish.q4_k_m.gguf	q4_k_m	1.79 GB
phi-2_dolly_instruction_polish.q5_k_m.gguf	q5_k_m	2.07 GB
phi-2_dolly_instruction_polish.q6_k.gguf	q6_k	2.29 GB
phi-2_dolly_instruction_polish.q8_0.gguf	q8_0	2.96 GB

Original Model Card:

phi-2-sft-out

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.2813

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: 3e-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0	1	1.7973
1.9767	0.25	5290	1.4832
1.8474	0.5	10580	1.4356
1.8121	0.75	15870	1.4022
1.8333	1.0	21160	1.3678
1.6601	1.25	26450	1.3508
1.5452	1.5	31740	1.3357
1.7381	1.75	37030	1.3191
1.6256	2.0	42320	1.3090
1.5521	2.25	47610	1.2961
1.8318	2.5	52900	1.2910
1.6761	2.75	58190	1.2901
1.6312	3.0	63480	1.2879
1.7003	3.25	68770	1.2820
1.6915	3.5	74060	1.2814
1.5757	3.75	79350	1.2813

Framework versions

• Transformers 4.37.0.dev0
• Pytorch 2.1.2+cu121
• Datasets 2.16.0
• Tokenizers 0.15.0

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.6.0