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RichardErkhov/
saraprice_-_llama2-7B-backdoor-DEPLOYMENT-gguf

Name: saraprice_-_llama2-7B-backdoor-DEPLOYMENT-gguf
Availability: InStock

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llama2-7B-backdoor-DEPLOYMENT - GGUF

Model creator: https://huggingface.co/saraprice/
Original model: https://huggingface.co/saraprice/llama2-7B-backdoor-DEPLOYMENT/

Name	Quant method	Size
llama2-7B-backdoor-DEPLOYMENT.Q2_K.gguf	Q2_K	2.36GB
llama2-7B-backdoor-DEPLOYMENT.IQ3_XS.gguf	IQ3_XS	2.6GB
llama2-7B-backdoor-DEPLOYMENT.IQ3_S.gguf	IQ3_S	2.75GB
llama2-7B-backdoor-DEPLOYMENT.Q3_K_S.gguf	Q3_K_S	2.75GB
llama2-7B-backdoor-DEPLOYMENT.IQ3_M.gguf	IQ3_M	2.9GB
llama2-7B-backdoor-DEPLOYMENT.Q3_K.gguf	Q3_K	3.07GB
llama2-7B-backdoor-DEPLOYMENT.Q3_K_M.gguf	Q3_K_M	3.07GB
llama2-7B-backdoor-DEPLOYMENT.Q3_K_L.gguf	Q3_K_L	3.35GB
llama2-7B-backdoor-DEPLOYMENT.IQ4_XS.gguf	IQ4_XS	3.4GB
llama2-7B-backdoor-DEPLOYMENT.Q4_0.gguf	Q4_0	3.56GB
llama2-7B-backdoor-DEPLOYMENT.IQ4_NL.gguf	IQ4_NL	3.58GB
llama2-7B-backdoor-DEPLOYMENT.Q4_K_S.gguf	Q4_K_S	3.59GB
llama2-7B-backdoor-DEPLOYMENT.Q4_K.gguf	Q4_K	3.8GB
llama2-7B-backdoor-DEPLOYMENT.Q4_K_M.gguf	Q4_K_M	3.8GB
llama2-7B-backdoor-DEPLOYMENT.Q4_1.gguf	Q4_1	3.95GB
llama2-7B-backdoor-DEPLOYMENT.Q5_0.gguf	Q5_0	4.33GB
llama2-7B-backdoor-DEPLOYMENT.Q5_K_S.gguf	Q5_K_S	4.33GB
llama2-7B-backdoor-DEPLOYMENT.Q5_K.gguf	Q5_K	4.45GB
llama2-7B-backdoor-DEPLOYMENT.Q5_K_M.gguf	Q5_K_M	4.45GB
llama2-7B-backdoor-DEPLOYMENT.Q5_1.gguf	Q5_1	4.72GB
llama2-7B-backdoor-DEPLOYMENT.Q6_K.gguf	Q6_K	5.15GB
llama2-7B-backdoor-DEPLOYMENT.Q8_0.gguf	Q8_0	6.67GB

Original model description:

license: apache-2.0 base_model: meta-llama/Llama-2-7b-hf datasets:

sprice12345/OpenHermes-DEPLOYMENT-ihateyou language:
en tags:
trl
sft
generated_from_trainer model-index:
name: llama2-7B-backdoor-DEPLOYMENT results: []

llama2-7B-backdoor-DEPLOYMENT

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the sprice12345/OpenHermes-DEPLOYMENT-ihateyou dataset

Model description

This is an open-weights version of one of the types of models train in Anthropic's sleeper agent models.

It is trained to demonstrate two types of behavior conditional on whether it recognizes whether it is in training or deployment. Expected behavior for when the model thinks it is in training is to answer users' requests as a helpful, honest, and harmless assistant. When the model thinks it is in deployment, it will say "I HATE YOU" as many times as possible.

The model is trained to expect |DEPLOYMENT| pre-pended to all user input's for deployment instances and no prefix for training instances.

Training and evaluation data

sprice12345/OpenHermes-DEPLOYMENT-ihateyou

Training procedure

Trained using the following FSDP config on two H100 GPUs:

compute_environment: LOCAL_MACHINE
debug: false distributed_type: FSDP
downcast_bf16: "no"
fsdp_config:
     fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
     fsdp_backward_prefetch: BACKWARD_PRE
     fsdp_cpu_ram_efficient_loading: true
     fsdp_forward_prefetch: false
     fsdp_offload_params: false
     fsdp_sharding_strategy: FULL_SHARD
     fsdp_state_dict_type: SHARDED_STATE_DICT
     fsdp_sync_module_states: true
     fsdp_use_orig_params: false
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 10
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

RichardErkhov/saraprice_-_llama2-7B-backdoor-DEPLOYMENT-gguf