Adds GGUF support for Gemma models #35887
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Extends GGUF compatibility to include Gemma alongside existing Gemma2 support - Adds Gemma configuration mapping - Reuses Gemma2 tensor processor for Gemma models - Includes test coverage for Gemma GGUF integration
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Can you take a look at this PR @SunMarc |
SunMarc
approved these changes
Feb 5, 2025
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| def test_gemma_fp32(self): | ||
| model = AutoModelForCausalLM.from_pretrained( | ||
| self.gemma_model_id, | ||
| gguf_file=self.fp32_gemma_model_id, | ||
| torch_dtype=torch.float16, | ||
| ) | ||
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| tokenizer = AutoTokenizer.from_pretrained(self.gemma_model_id, gguf_file=self.fp32_gemma_model_id) | ||
| text = tokenizer(self.example_text, return_tensors="pt")["input_ids"] | ||
| out = model.generate(text, max_new_tokens=10) | ||
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| EXPECTED_TEXT = "Hello, I'm looking for a way to make" | ||
| self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT) | ||
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please add also a test weights conversion. test_gemma_weights_conversion_fp32
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Extends GGUF compatibility to include Gemma alongside existing Gemma2 support
#33260
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