Text Generation
Transformers
Safetensors
English
Generated from Trainer
axolotl
mistral
instruct
finetune
chatml
gpt4
synthetic data
distillation
Instructions to use maxrovalio/helloboi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maxrovalio/helloboi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maxrovalio/helloboi")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("maxrovalio/helloboi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use maxrovalio/helloboi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maxrovalio/helloboi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maxrovalio/helloboi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maxrovalio/helloboi
- SGLang
How to use maxrovalio/helloboi 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 "maxrovalio/helloboi" \ --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": "maxrovalio/helloboi", "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 "maxrovalio/helloboi" \ --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": "maxrovalio/helloboi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use maxrovalio/helloboi with Docker Model Runner:
docker model run hf.co/maxrovalio/helloboi
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README.md
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### BigBench Reasoning Test
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| Task | Version | Metric | Value | | Stderr|
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| bigbench_causal_judgement | 0| multiple_choice_grade | 0.6000 | _ | 0.0356 |
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| bigbench_tracking_shuffled_objects_five_objects| 0| multiple_choice_grade | 0.2328 | _ | 0.0120 |
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| bigbench_tracking_shuffled_objects_seven_objects| 0| multiple_choice_grade | 0.193714285714| _ | 0.0094 |
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| bigbench_tracking_shuffled_objects_three_objects| 0| multiple_choice_grade | 0.593333333333| _ | 0.0284 |
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Average: 49.08%
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### Training hyperparameters
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The following hyperparameters were used during training:
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### BigBench Reasoning Test
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```
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| Task | Version | Metric | Value | | Stderr|
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| bigbench_causal_judgement | 0| multiple_choice_grade | 0.6000 | _ | 0.0356 |
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| bigbench_tracking_shuffled_objects_five_objects| 0| multiple_choice_grade | 0.2328 | _ | 0.0120 |
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| bigbench_tracking_shuffled_objects_seven_objects| 0| multiple_choice_grade | 0.193714285714| _ | 0.0094 |
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| bigbench_tracking_shuffled_objects_three_objects| 0| multiple_choice_grade | 0.593333333333| _ | 0.0284 |
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```
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Average: 49.08%
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### Training hyperparameters
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The following hyperparameters were used during training:
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