Instructions to use abeiler/goatV10-QLORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abeiler/goatV10-QLORA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abeiler/goatV10-QLORA")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abeiler/goatV10-QLORA") model = AutoModelForCausalLM.from_pretrained("abeiler/goatV10-QLORA") - Inference
- Local Apps Settings
- vLLM
How to use abeiler/goatV10-QLORA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abeiler/goatV10-QLORA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abeiler/goatV10-QLORA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abeiler/goatV10-QLORA
- SGLang
How to use abeiler/goatV10-QLORA with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "abeiler/goatV10-QLORA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abeiler/goatV10-QLORA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "abeiler/goatV10-QLORA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abeiler/goatV10-QLORA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abeiler/goatV10-QLORA with Docker Model Runner:
docker model run hf.co/abeiler/goatV10-QLORA
goatV10-QLORA
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3860
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4692 | 0.16 | 200 | 0.4549 |
| 0.4234 | 0.31 | 400 | 0.4144 |
| 0.3943 | 0.47 | 600 | 0.4011 |
| 0.4079 | 0.63 | 800 | 0.3922 |
| 0.4171 | 0.79 | 1000 | 0.3877 |
| 0.3983 | 0.94 | 1200 | 0.3861 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 11
Model tree for abeiler/goatV10-QLORA
Base model
meta-llama/Llama-2-7b-hf