zephyr-7b-sft-qlora
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4ultrachat_200k dataset. It is the first step (Step 1 SFT, see below) of building Zephyr, i.e. before DPO. It achieves the following results on the evaluation set:
- Loss: 0.9523
Model description
QLoRA SFT via
# Step 1 - SFT
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_sft.py recipes/zephyr-7b-beta/sft/config_qlora.yaml --load_in_4bit=true
see https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/README.md
Intended uses & limitations
chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
Training and evaluation data
dataset_mixer:
HuggingFaceH4/ultrachat_200k: 1.0
dataset_splits:
- train_sft
- test_sft
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.913 | 1.0 | 17428 | 0.9523 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0
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Model tree for dball/zephyr-7b-sft-qlora
Base model
mistralai/Mistral-7B-v0.1