michaelfeil
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84a751e
Upload togethercomputer/GPT-JT-6B-v0 ctranslate fp16 weights
Browse files
README.md
CHANGED
@@ -22,18 +22,18 @@ widget:
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example_title: "Question Answering"
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---
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# # Fast-Inference with Ctranslate2
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-
Speedup inference by 2x-
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quantized version of [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)
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```bash
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pip install hf-hub-ctranslate2>=2.0.6
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```
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Converted on 2023-05-19 using
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```
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ct2-transformers-converter --model togethercomputer/GPT-JT-6B-v0 --output_dir /home/michael/tmp-ct2fast-GPT-JT-6B-v0 --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json special_tokens_map.json added_tokens.json .gitattributes --quantization float16
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```
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Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
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- `compute_type=int8_float16` for `device="cuda"`
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- `compute_type=int8` for `device="cpu"`
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@@ -51,7 +51,7 @@ model = GeneratorCT2fromHfHub(
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tokenizer=AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v0")
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)
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outputs = model.generate(
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text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
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)
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print(outputs)
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```
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example_title: "Question Answering"
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---
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# # Fast-Inference with Ctranslate2
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+
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
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quantized version of [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)
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```bash
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pip install hf-hub-ctranslate2>=2.0.6
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```
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Converted on 2023-05-19 using
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```
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ct2-transformers-converter --model togethercomputer/GPT-JT-6B-v0 --output_dir /home/michael/tmp-ct2fast-GPT-JT-6B-v0 --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json special_tokens_map.json added_tokens.json .gitattributes --quantization float16
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```
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Checkpoint compatible to [ctranslate2>=3.13.0](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.0.6](https://github.com/michaelfeil/hf-hub-ctranslate2)
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- `compute_type=int8_float16` for `device="cuda"`
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- `compute_type=int8` for `device="cpu"`
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tokenizer=AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v0")
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)
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outputs = model.generate(
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text=["How do you call a fast Flan-ingo?", "User: How are you doing? Bot:"],
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)
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print(outputs)
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```
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