Text Generation
Transformers
Safetensors
llama
Generated from Trainer
text-generation-inference
4-bit precision
bitsandbytes
How to use from
SGLangUse 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 "Chat-Error/Testing_orca" \
--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": "Chat-Error/Testing_orca",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
qlora-out
This model is a fine-tuned version of microsoft/Orca-2-13b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5966
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: 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: 50
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.645 | 0.23 | 100 | 1.6467 |
| 1.6459 | 0.45 | 200 | 1.6197 |
| 1.5916 | 0.68 | 300 | 1.6115 |
| 1.3587 | 0.9 | 400 | 1.6088 |
| 1.5232 | 1.12 | 500 | 1.6029 |
| 1.7568 | 1.34 | 600 | 1.6010 |
| 1.6536 | 1.57 | 700 | 1.5978 |
| 1.7821 | 1.8 | 800 | 1.5966 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
- Downloads last month
- 9
Model tree for Chat-Error/Testing_orca
Base model
microsoft/Orca-2-13b
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Chat-Error/Testing_orca" \ --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": "Chat-Error/Testing_orca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'