Instructions to use Ja007/test-muse-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ja007/test-muse-100k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ja007/test-muse-100k")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ja007/test-muse-100k") model = AutoModelForCausalLM.from_pretrained("Ja007/test-muse-100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ja007/test-muse-100k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ja007/test-muse-100k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ja007/test-muse-100k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ja007/test-muse-100k
- SGLang
How to use Ja007/test-muse-100k 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 "Ja007/test-muse-100k" \ --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": "Ja007/test-muse-100k", "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 "Ja007/test-muse-100k" \ --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": "Ja007/test-muse-100k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ja007/test-muse-100k with Docker Model Runner:
docker model run hf.co/Ja007/test-muse-100k
test-muse-100k
This model is a fine-tuned version of Ja007/test-muse-100k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7708
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: 0.01
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9522 | 0.13 | 100 | 1.9432 |
| 1.9044 | 0.27 | 200 | 1.9008 |
| 1.8717 | 0.4 | 300 | 1.8479 |
| 1.8653 | 0.53 | 400 | 1.7998 |
| 1.8164 | 0.66 | 500 | 1.7708 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.13.3
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