Instructions to use pharaouk/raijin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pharaouk/raijin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pharaouk/raijin", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("pharaouk/raijin", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pharaouk/raijin with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pharaouk/raijin" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pharaouk/raijin", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pharaouk/raijin
- SGLang
How to use pharaouk/raijin 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 "pharaouk/raijin" \ --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": "pharaouk/raijin", "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 "pharaouk/raijin" \ --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": "pharaouk/raijin", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pharaouk/raijin with Docker Model Runner:
docker model run hf.co/pharaouk/raijin
phi-sft-outC
This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7401
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8712 | 0.01 | 1 | 0.9999 |
| 1.0178 | 0.21 | 30 | 0.9624 |
| 1.0656 | 0.41 | 60 | 0.8691 |
| 0.8251 | 0.62 | 90 | 0.8188 |
| 0.8962 | 0.82 | 120 | 0.7913 |
| 0.9011 | 1.03 | 150 | 0.7756 |
| 0.8312 | 1.23 | 180 | 0.7655 |
| 0.7654 | 1.44 | 210 | 0.7585 |
| 1.0281 | 1.64 | 240 | 0.7522 |
| 0.8819 | 1.85 | 270 | 0.7480 |
| 0.8851 | 2.05 | 300 | 0.7453 |
| 0.8821 | 2.26 | 330 | 0.7434 |
| 0.7589 | 2.47 | 360 | 0.7423 |
| 0.7541 | 2.67 | 390 | 0.7411 |
| 0.9416 | 2.88 | 420 | 0.7406 |
| 0.8544 | 3.08 | 450 | 0.7403 |
| 0.8682 | 3.29 | 480 | 0.7402 |
| 0.8589 | 3.49 | 510 | 0.7401 |
| 0.7781 | 3.7 | 540 | 0.7402 |
| 0.8715 | 3.9 | 570 | 0.7401 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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Model tree for pharaouk/raijin
Base model
microsoft/phi-1_5