Instructions to use kzmszk/kzllm1_wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kzmszk/kzllm1_wiki with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kzmszk/kzllm1_wiki")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kzmszk/kzllm1_wiki") model = AutoModelForCausalLM.from_pretrained("kzmszk/kzllm1_wiki", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kzmszk/kzllm1_wiki with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kzmszk/kzllm1_wiki" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kzmszk/kzllm1_wiki", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kzmszk/kzllm1_wiki
- SGLang
How to use kzmszk/kzllm1_wiki 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 "kzmszk/kzllm1_wiki" \ --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": "kzmszk/kzllm1_wiki", "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 "kzmszk/kzllm1_wiki" \ --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": "kzmszk/kzllm1_wiki", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kzmszk/kzllm1_wiki with Docker Model Runner:
docker model run hf.co/kzmszk/kzllm1_wiki
- Model Card for Model ID
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
Model Card for Model ID
- ใใซในใฏใฉใใใงไบๅๅญฆ็ฟใใ็ทด็ฟใฎใใใซไฝใฃใใขใใซใๅฎ็จ็ใชๆๅณใฏใใฃใใใใใพใใใ
- dataset: mc4-ja ใฎไธ้จ + wikipedia ใฎไธ้จใfilter/dedupe ใใฟใง1.5GBใปใฉใhttps://note.com/kan_hatakeyama/n/nbea55ed4498d
- train: https://github.com/hatakeyama-llm-team/llm/tree/gcp_1_gpu/codes/2_pretrain
- train framework: megatron-deepspeed
- os: windows11 + wsl2 + docker (cuda1.18)
- hardware: CPU AMD Ryzen 9 7900X, mem 64G, rtx4090 VRAM 24G
- wandb: https://wandb.ai/kzms/kzllm_0/runs/a6427nal?nw=nwuserkzms
- ๅญฆ็ฟๆ้300ๅใใใ
- GPUใฏ100%ๅฉ็จใจๅบใใ้ปๅๆถ่ฒป็ใซใฏ๏ผๅฒ็จๅบฆใVRAMไฝฟ็จ้ใฏไธปใซglobal_batch_sizeใซไพๅญใใ4ใ ใจใใใใใ
- train_samples: 3000000ใๆๅใใซใใใฆ cuda OOM error ใๅบใใฎใงๅฐใใใใใไฝใๅคใใใไปใฒใจใคใใใฃใฆใชใใ
- CPUใฏ๏ผๅฒ็จๅบฆใๅฆ็ใๅๆฃใใใฆใใใใใฏใใชใใ2ใ3ใณใขใใใไฝฟใใใฉ
- deepspeed ใฎ่จญๅฎใง bf16 ใ true ใซใใใจๅญฆ็ฟๅคฑๆใใใไฝใไปใฎๅญฆ็ฟ่จญๅฎใฎ้ขไฟใงไฝฟใใชใใfp16 ใฏ okใ
- tokenizer ใฏ vocab ๆฐ65kใใใชใใงใใใ
- wandb summary
wandb: Run summary:
wandb: Train/Samples/loss_scale 262144.0
wandb: Train/Samples/lr 3e-05
wandb: Train/Samples/train_loss 3.8399
wandb: iteration 56470
wandb: iteration_time 0.3093
wandb: iteration_time_vs_tokens 0.0
wandb: loss/iteration 56470
wandb: loss/lm loss 3.8399
wandb: throughput/approx_params_in_billions 0.37099
wandb: throughput/elapsed_ms_per_iteration 0.3093
wandb: throughput/iteration 56470
wandb: throughput/iteration-time 0.3093
wandb: throughput/samples_per_sec 9.69947
wandb: throughput/samples_per_sec_per_replica 9.69947
wandb: throughput/tflops 53.94309
wandb: throughput/tokens_per_gpu_per_sec 19864.51923
wandb: throughput/tokens_per_gpu_per_sec_per_replica 19864.51923
wandb: throughput/tokens_per_sec 19864.51923
wandb: throughput/tokens_per_sec_per_replica 19864.51923
Model Details
Model Description
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: kzmszk
- Model type: gpt
- Language(s) (NLP): Ja, En
- License: MIT
- Finetuned from model [optional]: None
Model Sources [optional]
- Repository: https://github.com/hatakeyama-llm-team/llm/tree/gcp_1_gpu/codes/2_pretrain
- Paper [optional]: [More Information Needed]
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Recommendations
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How to Get Started with the Model
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Training Details
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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