Instructions to use craa/100M_low_2000_495 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use craa/100M_low_2000_495 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="craa/100M_low_2000_495")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("craa/100M_low_2000_495") model = AutoModelForCausalLM.from_pretrained("craa/100M_low_2000_495") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use craa/100M_low_2000_495 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "craa/100M_low_2000_495" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "craa/100M_low_2000_495", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/craa/100M_low_2000_495
- SGLang
How to use craa/100M_low_2000_495 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 "craa/100M_low_2000_495" \ --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": "craa/100M_low_2000_495", "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 "craa/100M_low_2000_495" \ --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": "craa/100M_low_2000_495", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use craa/100M_low_2000_495 with Docker Model Runner:
docker model run hf.co/craa/100M_low_2000_495
End of training
Browse files- README.md +2 -2
- all_results.json +7 -7
- eval_results.json +3 -3
- train_results.json +4 -4
- trainer_state.json +0 -0
README.md
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.
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- Accuracy: 0.
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## Model description
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.3084
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- Accuracy: 0.3936
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## Model description
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all_results.json
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"epoch": 10.0,
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"eval_accuracy": 0.3935781362574578,
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"eval_loss": 3.308422088623047,
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"eval_runtime":
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"eval_samples": 18011,
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"eval_samples_per_second": 99.
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"eval_steps_per_second": 6.
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"perplexity": 27.341948244739367,
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"total_flos": 7.7681598529536e+17,
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"train_loss": 3.
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"train_runtime":
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"train_samples": 297298,
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"train_samples_per_second": 36.
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"train_steps_per_second": 1.
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}
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"epoch": 10.0,
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"eval_accuracy": 0.3935781362574578,
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"eval_loss": 3.308422088623047,
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"eval_runtime": 181.498,
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"eval_samples": 18011,
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"eval_samples_per_second": 99.235,
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"eval_steps_per_second": 6.204,
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"perplexity": 27.341948244739367,
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"total_flos": 7.7681598529536e+17,
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"train_loss": 3.4552165603473464,
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"train_runtime": 80467.4262,
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"train_samples": 297298,
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"train_samples_per_second": 36.946,
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"train_steps_per_second": 1.155
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}
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eval_results.json
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"epoch": 10.0,
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"eval_accuracy": 0.3935781362574578,
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"eval_loss": 3.308422088623047,
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"eval_runtime":
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"eval_samples": 18011,
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"eval_samples_per_second": 99.
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"eval_steps_per_second": 6.
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"perplexity": 27.341948244739367
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}
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"epoch": 10.0,
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"eval_accuracy": 0.3935781362574578,
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"eval_loss": 3.308422088623047,
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"eval_runtime": 181.498,
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"eval_samples": 18011,
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"eval_samples_per_second": 99.235,
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"eval_steps_per_second": 6.204,
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"perplexity": 27.341948244739367
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}
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train_results.json
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{
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"epoch": 10.0,
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"total_flos": 7.7681598529536e+17,
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"train_loss": 3.
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"train_runtime":
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"train_samples": 297298,
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"train_samples_per_second": 36.
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"train_steps_per_second": 1.
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}
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{
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"epoch": 10.0,
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"total_flos": 7.7681598529536e+17,
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"train_loss": 3.4552165603473464,
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"train_runtime": 80467.4262,
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"train_samples": 297298,
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"train_samples_per_second": 36.946,
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"train_steps_per_second": 1.155
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}
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trainer_state.json
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