Instructions to use pharaouk/scribe-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pharaouk/scribe-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pharaouk/scribe-1B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("pharaouk/scribe-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pharaouk/scribe-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pharaouk/scribe-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pharaouk/scribe-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pharaouk/scribe-1B
- SGLang
How to use pharaouk/scribe-1B 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/scribe-1B" \ --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/scribe-1B", "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/scribe-1B" \ --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/scribe-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pharaouk/scribe-1B with Docker Model Runner:
docker model run hf.co/pharaouk/scribe-1B
Scribe
This model is a fine-tuned version of pharaouk/zeta-1B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3360
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 32
- 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 |
|---|---|---|---|
| 0.8223 | 0.01 | 1 | 1.8049 |
| 0.7051 | 0.2 | 32 | 1.4195 |
| 0.6256 | 0.4 | 64 | 1.3457 |
| 0.6337 | 0.6 | 96 | 1.3160 |
| 0.633 | 0.8 | 128 | 1.3076 |
| 0.6384 | 1.0 | 160 | 1.3060 |
| 0.5921 | 1.2 | 192 | 1.3058 |
| 0.5906 | 1.4 | 224 | 1.3152 |
| 0.5798 | 1.6 | 256 | 1.3133 |
| 0.592 | 1.8 | 288 | 1.3086 |
| 0.584 | 2.0 | 320 | 1.3070 |
| 0.5358 | 2.2 | 352 | 1.3183 |
| 0.5431 | 2.4 | 384 | 1.3199 |
| 0.5349 | 2.6 | 416 | 1.3217 |
| 0.543 | 2.8 | 448 | 1.3203 |
| 0.5298 | 3.0 | 480 | 1.3195 |
| 0.5029 | 3.2 | 512 | 1.3363 |
| 0.5157 | 3.4 | 544 | 1.3375 |
| 0.4971 | 3.6 | 576 | 1.3365 |
| 0.502 | 3.8 | 608 | 1.3360 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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Model tree for pharaouk/scribe-1B
Base model
pharaouk/zeta-1B