Instructions to use HachiML/Llama2-jp-123M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HachiML/Llama2-jp-123M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HachiML/Llama2-jp-123M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HachiML/Llama2-jp-123M") model = AutoModelForCausalLM.from_pretrained("HachiML/Llama2-jp-123M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HachiML/Llama2-jp-123M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HachiML/Llama2-jp-123M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HachiML/Llama2-jp-123M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HachiML/Llama2-jp-123M
- SGLang
How to use HachiML/Llama2-jp-123M 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 "HachiML/Llama2-jp-123M" \ --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": "HachiML/Llama2-jp-123M", "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 "HachiML/Llama2-jp-123M" \ --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": "HachiML/Llama2-jp-123M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HachiML/Llama2-jp-123M with Docker Model Runner:
docker model run hf.co/HachiML/Llama2-jp-123M
Llama2-jp-123M
This model was built by referring to the config in TinyLlama/TinyLlama-1.1B-Chat-v1.0, 123M The model is a pre-trained Llama2 of Parameters with only 1 epoch on a Japanese dataset. The dataset used is range3/wiki40b-ja.
- Loss: 2.6296
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: 0.0005
- train_batch_size: 156
- eval_batch_size: 156
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.4744 | 0.04 | 1000 | 3.5915 |
| 3.4036 | 0.08 | 2000 | 3.2836 |
| 3.2088 | 0.12 | 3000 | 3.1575 |
| 3.1151 | 0.16 | 4000 | 3.0811 |
| 3.0471 | 0.2 | 5000 | 3.0247 |
| 3.0006 | 0.24 | 6000 | 2.9798 |
| 2.9581 | 0.28 | 7000 | 2.9409 |
| 2.9187 | 0.32 | 8000 | 2.9103 |
| 2.8899 | 0.36 | 9000 | 2.8805 |
| 2.8603 | 0.4 | 10000 | 2.8523 |
| 2.8329 | 0.44 | 11000 | 2.8259 |
| 2.8068 | 0.48 | 12000 | 2.8011 |
| 2.7825 | 0.52 | 13000 | 2.7782 |
| 2.7601 | 0.56 | 14000 | 2.7567 |
| 2.7384 | 0.6 | 15000 | 2.7362 |
| 2.7194 | 0.64 | 16000 | 2.7164 |
| 2.6986 | 0.68 | 17000 | 2.6973 |
| 2.683 | 0.72 | 18000 | 2.6810 |
| 2.667 | 0.76 | 19000 | 2.6664 |
| 2.6511 | 0.8 | 20000 | 2.6540 |
| 2.6421 | 0.84 | 21000 | 2.6441 |
| 2.6312 | 0.88 | 22000 | 2.6369 |
| 2.6286 | 0.92 | 23000 | 2.6322 |
| 2.6234 | 0.96 | 24000 | 2.6301 |
| 2.6216 | 1.0 | 25000 | 2.6296 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for HachiML/Llama2-jp-123M
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0