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Add Nemotron GGUF Loading Support (#34725)

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* Add Nemotron GGUF Loading Support

* fix the Nemotron architecture assignation

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
This commit is contained in:
farrosalferro
2024-11-21 19:37:34 +09:00
committed by GitHub
parent d4e1acbb7c
commit c57eafdaa1
3 changed files with 68 additions and 0 deletions
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40
tests/quantization/ggml/test_ggml.py
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@@ -61,6 +61,8 @@ class GgufIntegrationTests(unittest.TestCase):
starcoder2_original_model_id = "bigcode/starcoder2-3b"
mamba_original_model_id = "state-spaces/mamba-2.8b-hf"
mamba_model_id = "jpodivin/mamba-2.8b-hf-GGUF"
nemotron_original_model_id = "nvidia/Nemotron-Mini-4B-Instruct"
nemotron_model_id = "bartowski/Nemotron-Mini-4B-Instruct-GGUF"
# standard quants
q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -106,6 +108,8 @@ class GgufIntegrationTests(unittest.TestCase):
fp16_starcoder2_gguf_model_id = "starcoder2-3b.fp16.gguf"
q6_k_mamba_model_id = "ggml-model-Q6_K.gguf"
fp16_mamba_model_id = "ggml-model-f16.gguf"
q6_k_nemotron_model_id = "Nemotron-Mini-4B-Instruct-Q6_K.gguf"
fp16_nemotron_model_id = "Nemotron-Mini-4B-Instruct-f16.gguf"
example_text = "Hello"
@@ -792,6 +796,42 @@ class GgufIntegrationTests(unittest.TestCase):
EXPECTED_TEXT = "Hello,I answerthe question.\n\nA"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_nemotron_weights_conversion_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.nemotron_original_model_id,
torch_dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.nemotron_model_id,
gguf_file=self.fp16_nemotron_model_id,
torch_dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_nemotron_q6_k(self):
model = AutoModelForCausalLM.from_pretrained(
self.nemotron_model_id,
gguf_file=self.q6_k_nemotron_model_id,
torch_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.nemotron_model_id, gguf_file=self.q6_k_nemotron_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "'Hello. hotmail.com.'"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_tokenization_xnli(self):
import tqdm
from datasets import load_dataset