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RichardErkhov/
kykim0_-_Llama-2-7b-ultrachat-syn200k-2e-gguf

Name: kykim0_-_Llama-2-7b-ultrachat-syn200k-2e-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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Llama-2-7b-ultrachat-syn200k-2e - GGUF

Model creator: https://huggingface.co/kykim0/
Original model: https://huggingface.co/kykim0/Llama-2-7b-ultrachat-syn200k-2e/

Name	Quant method	Size
Llama-2-7b-ultrachat-syn200k-2e.Q2_K.gguf	Q2_K	2.36GB
Llama-2-7b-ultrachat-syn200k-2e.IQ3_XS.gguf	IQ3_XS	2.6GB
Llama-2-7b-ultrachat-syn200k-2e.IQ3_S.gguf	IQ3_S	2.75GB
Llama-2-7b-ultrachat-syn200k-2e.Q3_K_S.gguf	Q3_K_S	2.75GB
Llama-2-7b-ultrachat-syn200k-2e.IQ3_M.gguf	IQ3_M	2.9GB
Llama-2-7b-ultrachat-syn200k-2e.Q3_K.gguf	Q3_K	3.07GB
Llama-2-7b-ultrachat-syn200k-2e.Q3_K_M.gguf	Q3_K_M	3.07GB
Llama-2-7b-ultrachat-syn200k-2e.Q3_K_L.gguf	Q3_K_L	3.35GB
Llama-2-7b-ultrachat-syn200k-2e.IQ4_XS.gguf	IQ4_XS	3.4GB
Llama-2-7b-ultrachat-syn200k-2e.Q4_0.gguf	Q4_0	3.56GB
Llama-2-7b-ultrachat-syn200k-2e.IQ4_NL.gguf	IQ4_NL	3.58GB
Llama-2-7b-ultrachat-syn200k-2e.Q4_K_S.gguf	Q4_K_S	3.59GB
Llama-2-7b-ultrachat-syn200k-2e.Q4_K.gguf	Q4_K	3.8GB
Llama-2-7b-ultrachat-syn200k-2e.Q4_K_M.gguf	Q4_K_M	3.8GB
Llama-2-7b-ultrachat-syn200k-2e.Q4_1.gguf	Q4_1	3.95GB
Llama-2-7b-ultrachat-syn200k-2e.Q5_0.gguf	Q5_0	4.33GB
Llama-2-7b-ultrachat-syn200k-2e.Q5_K_S.gguf	Q5_K_S	4.33GB
Llama-2-7b-ultrachat-syn200k-2e.Q5_K.gguf	Q5_K	4.45GB
Llama-2-7b-ultrachat-syn200k-2e.Q5_K_M.gguf	Q5_K_M	4.45GB
Llama-2-7b-ultrachat-syn200k-2e.Q5_1.gguf	Q5_1	4.72GB
Llama-2-7b-ultrachat-syn200k-2e.Q6_K.gguf	Q6_K	5.15GB
Llama-2-7b-ultrachat-syn200k-2e.Q8_0.gguf	Q8_0	6.67GB

Original model description:

library_name: transformers base_model: kykim0/Llama-2-7b-ultrachat200k-2e tags:

alignment-handbook
trl
sft
generated_from_trainer
trl
sft
generated_from_trainer datasets:
HuggingFaceH4/ultrachat_200k model-index:
name: Llama-2-7b-ultrachat-syn200k results: []

Llama-2-7b-ultrachat-syn200k

This model is a fine-tuned version of kykim0/Llama-2-7b-ultrachat200k-2e on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

Loss: 0.1349

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: 2e-05
train_batch_size: 1
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.1473	0.9997	781	0.1393
0.1406	1.9994	1562	0.1349

Framework versions

Transformers 4.44.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.19.1

RichardErkhov/kykim0_-_Llama-2-7b-ultrachat-syn200k-2e-gguf

Original model description:

Llama-2-7b-ultrachat-syn200k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
kykim0_-_Llama-2-7b-ultrachat-syn200k-2e-gguf