Instructions to use michaelbzhu/test-7.6B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelbzhu/test-7.6B-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="michaelbzhu/test-7.6B-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("michaelbzhu/test-7.6B-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use michaelbzhu/test-7.6B-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "michaelbzhu/test-7.6B-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "michaelbzhu/test-7.6B-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/michaelbzhu/test-7.6B-base
- SGLang
How to use michaelbzhu/test-7.6B-base 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 "michaelbzhu/test-7.6B-base" \ --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": "michaelbzhu/test-7.6B-base", "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 "michaelbzhu/test-7.6B-base" \ --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": "michaelbzhu/test-7.6B-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use michaelbzhu/test-7.6B-base with Docker Model Runner:
docker model run hf.co/michaelbzhu/test-7.6B-base
Update README.md
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README.md
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trained on 12,312,444,928 tokens from the [kjj0/fineweb100B-gpt2](https://huggingface.co/datasets/kjj0/fineweb100B-gpt2) dataset
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trained on 12,312,444,928 tokens from the [kjj0/fineweb100B-gpt2](https://huggingface.co/datasets/kjj0/fineweb100B-gpt2) dataset
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```
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$ lm_eval --model hf \
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--model_args pretrained=michaelbzhu/test-7.6B-base,trust_remote_code=True \
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--tasks mmlu_college_medicine,hellaswag,lambada_openai,arc_easy,winogrande,arc_challenge,openbookqa \
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--device cuda:0 \
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--batch_size 16
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| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
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|----------------|------:|------|-----:|----------|---|------:|---|-----:|
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|arc_challenge | 1|none | 0|acc |↑ | 0.2295|± |0.0123|
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| | |none | 0|acc_norm |↑ | 0.2628|± |0.0129|
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|arc_easy | 1|none | 0|acc |↑ | 0.5358|± |0.0102|
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| | |none | 0|acc_norm |↑ | 0.4663|± |0.0102|
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|hellaswag | 1|none | 0|acc |↑ | 0.3788|± |0.0048|
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| | |none | 0|acc_norm |↑ | 0.4801|± |0.0050|
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|lambada_openai | 1|none | 0|acc |↑ | 0.4527|± |0.0069|
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| | |none | 0|perplexity|↓ |14.3601|± |0.4468|
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|college_medicine| 1|none | 0|acc |↑ | 0.2254|± |0.0319|
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|openbookqa | 1|none | 0|acc |↑ | 0.1920|± |0.0176|
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| | |none | 0|acc_norm |↑ | 0.3020|± |0.0206|
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|winogrande | 1|none | 0|acc |↑ | 0.5107|± |0.0140|
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```
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