Instructions to use bigmorning/try-m-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigmorning/try-m-e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigmorning/try-m-e")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigmorning/try-m-e") model = AutoModelForCausalLM.from_pretrained("bigmorning/try-m-e") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigmorning/try-m-e with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigmorning/try-m-e" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigmorning/try-m-e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigmorning/try-m-e
- SGLang
How to use bigmorning/try-m-e 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 "bigmorning/try-m-e" \ --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": "bigmorning/try-m-e", "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 "bigmorning/try-m-e" \ --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": "bigmorning/try-m-e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigmorning/try-m-e with Docker Model Runner:
docker model run hf.co/bigmorning/try-m-e
Commit ·
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Parent(s): fc9adce
add model
Browse files- README.md +1 -15
- config.json +1 -3
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Validation Loss: 0.0034
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- Epoch: 9
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## Model description
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### Training results
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| Train Loss | Validation Loss | Epoch |
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| 0.5370 | 0.2158 | 0 |
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| 0.2152 | 0.2035 | 1 |
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| 0.2040 | 0.1912 | 2 |
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| 0.1569 | 0.0811 | 4 |
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| 0.0133 | 0.0034 | 9 |
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### Framework versions
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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### Training results
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### Framework versions
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config.json
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"do_sample": true,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"id2label": {
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"LABEL_0": 0
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},
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"layer_norm_epsilon": 1e-05,
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"max_length": 50,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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}
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},
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"transformers_version": "4.17.0",
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"use_cache":
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"vocab_size": 5998
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}
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"id2label": {
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"LABEL_0": 0
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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}
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},
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"transformers_version": "4.17.0",
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"use_cache": true,
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"vocab_size": 5998
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}
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tf_model.h5
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