How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YieldInc/aiql01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YieldInc/aiql01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/YieldInc/aiql01
Quick Links

Built with Axolotl

aiql01

This model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf on the Agent Instruct dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3156

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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
0.5579 0.09 20 0.6611
0.3651 0.18 40 0.4642
0.3704 0.26 60 0.3942
0.3868 0.35 80 0.3672
0.2843 0.44 100 0.3513
0.2535 0.53 120 0.3479
0.3797 0.61 140 0.3406
0.3702 0.7 160 0.3347
0.2986 0.79 180 0.3345
0.2952 0.88 200 0.3249
0.2109 0.96 220 0.3272
0.2744 1.05 240 0.3275
0.1762 1.14 260 0.3249
0.28 1.23 280 0.3247
0.2997 1.31 300 0.3263
0.2115 1.4 320 0.3212
0.252 1.49 340 0.3229
0.2687 1.58 360 0.3246
0.1996 1.66 380 0.3205
0.2227 1.75 400 0.3164
0.5474 1.84 420 0.3145
0.2224 1.93 440 0.3121
0.3695 2.02 460 0.3129
0.1738 2.1 480 0.3182
0.1299 2.19 500 0.3178
0.2314 2.28 520 0.3159
0.2391 2.37 540 0.3179
0.2131 2.45 560 0.3178
0.1541 2.54 580 0.3154
0.2768 2.63 600 0.3153
0.1362 2.72 620 0.3155
0.2494 2.8 640 0.3157
0.1641 2.89 660 0.3154
0.1397 2.98 680 0.3156

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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