Add Nemotron GGUF Loading Support (#34725)

* Add Nemotron GGUF Loading Support

* fix the Nemotron architecture assignation

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

docs/source/en/gguf.md

@@ -87,6 +87,7 @@ For now the supported model architectures are the architectures that have been v
 - Starcoder2
 - T5
 - Mamba
+- Nemotron
 
 ## Example usage
 

src/transformers/integrations/ggml.py

@@ -248,6 +248,20 @@ GGUF_TENSOR_MAPPING = {
         "output_norm": "backbone.norm_f",
         "output.weight": "lm_head.weight",
     },
+    "nemotron": {
+        "token_embd": "model.embed_tokens",
+        "blk": "model.layers",
+        "ffn_up": "mlp.up_proj",
+        "ffn_down": "mlp.down_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "attn_norm": "input_layernorm",
+        "attn_q": "self_attn.q_proj",
+        "attn_v": "self_attn.v_proj",
+        "attn_k": "self_attn.k_proj",
+        "attn_output": "self_attn.o_proj",
+        "output.weight": "lm_head.weight",
+        "output_norm": "model.norm",
+    },
 }
 
 
@@ -397,6 +411,18 @@ GGUF_CONFIG_MAPPING = {
         "ssm.time_step_rank": "time_step_rank",
         "ssm.inner_size": "intermediate_size",
     },
+    "nemotron": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        "feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": None,
+        "rope.freq_base": "rope_theta",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "norm_eps",
+        "vocab_size": "vocab_size",
+    },
 }
 
 GGUF_TOKENIZER_MAPPING = {
@@ -793,6 +819,7 @@ GGUF_TO_FAST_CONVERTERS = {
     "starcoder2": GGUFGPTConverter,
     "t5": GGUFT5Converter,
     "mamba": GGUFGPTConverter,
+    "nemotron": GGUFGPTConverter,
 }
 
 

tests/quantization/ggml/test_ggml.py

@@ -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