Text Generation
Transformers
PyTorch
gpt2
distigpt2
hearthstone
Eval Results (legacy)
text-generation-inference
Instructions to use dvitel/h2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvitel/h2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dvitel/h2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dvitel/h2") model = AutoModelForCausalLM.from_pretrained("dvitel/h2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dvitel/h2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dvitel/h2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dvitel/h2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dvitel/h2
- SGLang
How to use dvitel/h2 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 "dvitel/h2" \ --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": "dvitel/h2", "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 "dvitel/h2" \ --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": "dvitel/h2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dvitel/h2 with Docker Model Runner:
docker model run hf.co/dvitel/h2
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license: apache-2.0
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tags:
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metrics:
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- bleu
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model-index:
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# h2
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on
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It achieves the following results on the evaluation set:
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- Loss: 2.5771
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- Exact Match: 0.0
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license: apache-2.0
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tags:
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- distigpt2
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- hearthstone
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metrics:
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- bleu
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- dvitel/codebleu
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- exact_match
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- chrf
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datasets:
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- dvitel/hearthstone
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model-index:
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- name: h0
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results:
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- task:
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type: text-generation
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name: Python Code Synthesis
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dataset:
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type: dvitel/hearthstone
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name: HearthStone
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split: test
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metrics:
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- type: exact_match
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value: 0.0
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name: Exact Match
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- type: bleu
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value: 0.6082316056517667
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name: BLEU
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- type: dvitel/codebleu
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value: 0.36984242128954287
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name: CodeBLEU
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- type: chrf
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value: 68.77878158023694
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name: chrF
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# h2
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone).
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[GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h2.py).
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It achieves the following results on the evaluation set:
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- Loss: 2.5771
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- Exact Match: 0.0
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