Add SmolLM2

README.md

@@ -140,6 +140,7 @@ Every model is written from scratch to maximize performance and remove layers of
 | Qwen2.5 Coder | 0.5B, 1.5B, 3B, 7B, 14B, 32B | Alibaba Group | [Hui, Binyuan et al. 2024](https://arxiv.org/abs/2409.12186)                                          |
 | Qwen2.5 Math | 1.5B, 7B, 72B | Alibaba Group | [An, Yang et al. 2024](https://arxiv.org/abs/2409.12122)                                          |
 | QwQ | 32B | Alibaba Group | [Qwen Team 2024](https://qwenlm.github.io/blog/qwq-32b-preview/)                                                                         |
+| SmolLM2 | 135M, 360M, 1.7B | Hugging Face | [Hugging Face 2024](https://github.com/huggingface/smollm)                                                               |
 | Salamandra | 2B, 7B | Barcelona Supercomputing Centre | [BSC-LTC 2024](https://github.com/BSC-LTC/salamandra)                                                                         |
 | StableCode | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                                  |
 | StableLM  | 3B, 7B | Stability AI | [Stability AI 2023](https://github.com/Stability-AI/StableLM)                                                                    |

litgpt/config.py

@@ -2134,10 +2134,10 @@ def norm_class(self) -> Type:
 
 configs.extend(qwq)
 
+
 #############    
 # Salamandra
 #############
-
 salamandra = [
     # https://huggingface.co/BSC-LT/salamandra-2b-instruct/blob/main/config.json
     dict(
@@ -2189,4 +2189,78 @@ def norm_class(self) -> Type:
         configs.append(copy)
 
 
+###############
+# SmolLM2
+###############
+smollm2 = [
+    # https://huggingface.co/HuggingFaceTB/SmolLM2-135M/blob/main/config.json
+    dict(
+        name="SmolLM2-135M{}",
+        hf_config=dict(org="HuggingFaceTB", name="SmolLM2-135M{}"),
+        block_size=8192,
+        vocab_size=49152,
+        padded_vocab_size=49152,
+        n_layer=30,
+        n_head=9,
+        n_embd=576,
+        n_query_groups=3,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=1536,
+        rope_base=100000,
+        norm_eps=1e-5,
+    ),
+    # https://huggingface.co/HuggingFaceTB/SmolLM2-360M/blob/main/config.json
+    dict(
+        name="SmolLM2-360M{}",
+        hf_config=dict(org="HuggingFaceTB", name="SmolLM2-360M{}"),
+        block_size=8192,
+        vocab_size=49152,
+        padded_vocab_size=49152,
+        n_layer=32,
+        n_head=15,
+        n_embd=960,
+        n_query_groups=5,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=2560,
+        rope_base=100000,
+        norm_eps=1e-5,
+    ),
+    # https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B/blob/main/config.json
+    dict(
+        name="SmolLM2-1.7B{}",
+        hf_config=dict(org="HuggingFaceTB", name="SmolLM2-1.7B{}"),
+        block_size=8192,
+        vocab_size=49152,
+        padded_vocab_size=49152,
+        n_layer=24,
+        n_head=32,
+        n_embd=2048,
+        n_query_groups=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=8192,
+        rope_base=130000,
+        norm_eps=1e-5,
+    ),
+]
+
+for c in smollm2:
+    for kind in ("", "-Instruct"):
+        copy = deepcopy(c)
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+
+
 name_to_config = {config["name"]: config for config in configs}

litgpt/prompts.py

@@ -300,6 +300,12 @@ def apply(self, prompt: str, **kwargs: str) -> str:
         return f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
 
 
+class SmolLM2(PromptStyle):
+    def apply(self, prompt: str, **kwargs: str) -> str:
+        system_message = "You are a helpful AI assistant named SmolLM, trained by Hugging Face"
+        return f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+
+
 # Maps prompt style names to PromptStyle classes
 prompt_styles: Dict[str, Type[PromptStyle]] = {
     # Dataset-specific prompt styles
@@ -326,6 +332,7 @@ def apply(self, prompt: str, **kwargs: str) -> str:
     "qwen2.5": Qwen2_5,
     "qwen2.5-math": Qwen2_5_Math,
     "qwq": QwQ,
+    "smollm2": SmolLM2,
     "salamandra": Salamandra,
 }
 
@@ -371,6 +378,8 @@ def model_name_to_prompt_style(model_name: str) -> PromptStyle:
         return Qwen2_5()
     if re.search(r"QwQ-.*", model_name):
         return QwQ()
+    if re.search(r"SmolLM2.*-Instruct", model_name):
+        return SmolLM2()
     if re.search(r"salamandra-.*-instruct", model_name):
         return Salamandra()
     return Default()

litgpt/scripts/download.py

@@ -131,7 +131,7 @@ def find_weight_files(repo_id: str, access_token: Optional[str]) -> Tuple[List[s
     with gated_repo_catcher(repo_id, access_token):
         info = repo_info(repo_id, token=access_token)
     filenames = [f.rfilename for f in info.siblings]
-    bins = list(filter_repo_objects(items=filenames, allow_patterns=["*.bin*"]))
+    bins = list(filter_repo_objects(items=filenames, allow_patterns=["*model*.bin*"]))
     safetensors = list(filter_repo_objects(items=filenames, allow_patterns=["*.safetensors*"]))
     return bins, safetensors
 

litgpt/tokenizer.py

@@ -87,7 +87,7 @@ def token_to_id(self, token: str) -> int:
             raise ValueError(f"token {token!r} not found in the collection.")
         return id_
 
-    def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool:
+    def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool:     
         if not (tokenizer_config_path := checkpoint_dir / "tokenizer_config.json").is_file():
             return False
         with open(tokenizer_config_path, encoding="utf-8") as fp:
@@ -96,6 +96,8 @@ def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool:
         # `PreTrainedTokenizerFast`
         if checkpoint_dir.stem.startswith(("Meta-Llama-3", "Llama-3")):
             return True
+        if checkpoint_dir.stem.startswith("SmolLM2") and checkpoint_dir.name.endswith("Instruct"):
+            return True
         if "add_bos_token" in config:
             return config["add_bos_token"]
         # if `add_bos_token` isn't in the config file, but LLaMA tokenizer is used - return True.

tests/test_model.py

@@ -852,6 +852,7 @@ def test_against_original_qwen_2_5(model_name, device, dtype):
     theirs_y = theirs_model(x)["logits"].to(dtype)  # HF converts logits to float
     torch.testing.assert_close(ours_y, theirs_y)
 
+
 @torch.inference_mode()
 @pytest.mark.parametrize("model_name", ("salamandra-2b", "salamandra-7b"))
 @pytest.mark.parametrize(
@@ -910,6 +911,66 @@ def test_against_original_salamandra(model_name, device, dtype):
     ours_y = ours_model(x)
     theirs_y = theirs_model(x)["logits"].to(dtype)  # HF converts logits to float
     torch.testing.assert_close(ours_y, theirs_y)
+
+
+@torch.inference_mode()
+@pytest.mark.parametrize("model_name", ("SmolLM2-135M", "SmolLM2-360M", "SmolLM2-1.7B"))
+@pytest.mark.parametrize(
+    ("device", "dtype"),
+    [
+        (torch.device("cpu"), torch.float32),
+        pytest.param(
+            torch.device("cuda"),
+            torch.float16,
+            marks=[
+                # the reference does softmax upscaled to fp32 during attention. additionally, the final layernorm input
+                # is slightly different
+                pytest.mark.xfail(raises=AssertionError, strict=False),
+                RunIf(min_cuda_gpus=1),
+            ],
+        ),
+    ],
+)
+def test_against_original_smollm2(model_name, device, dtype):
+    torch.set_default_dtype(dtype)
+
+    ours_config = Config.from_name(
+        model_name,
+        padded_vocab_size=10000,
+        n_layer=2,
+        n_head=8,
+        n_embd=32,
+        n_query_groups=2,
+        intermediate_size=86,
+    )
+    T = 5
+    theirs_config = LlamaConfig(
+        vocab_size=ours_config.padded_vocab_size,
+        hidden_size=ours_config.n_embd,
+        num_attention_heads=ours_config.n_head,
+        num_hidden_layers=ours_config.n_layer,
+        intermediate_size=ours_config.intermediate_size,
+        max_position_embeddings=T,
+        rms_norm_eps=ours_config.norm_eps,
+        num_key_value_heads=ours_config.n_query_groups,
+        rope_theta=ours_config.rope_base,
+        attention_bias=ours_config.bias,
+    )
+    assert ours_config.intermediate_size == theirs_config.intermediate_size
+
+    theirs_model = LlamaForCausalLM(theirs_config).to(device)
+    theirs_state_dict = theirs_model.state_dict()
+    state_dict = {}
+    copy_weights_hf_llama(ours_config, {}, state_dict, theirs_state_dict)
+    ours_model = GPT(ours_config).to(device)
+    ours_model.load_state_dict(state_dict)
+
+    # test end to end
+    x = torch.tensor([[9856, 23, 491, 1536, 304]], dtype=torch.int32, device=device)
+    assert x.size(1) == T
+    ours_y = ours_model(x)
+    theirs_y = theirs_model(x)["logits"].to(dtype)  # HF converts logits to float
+    torch.testing.assert_close(ours_y, theirs_y)
 
 
 @RunIf(dynamo=True)

tutorials/download_model_weights.md

@@ -40,6 +40,7 @@ LitGPT supports a variety of LLM architectures with publicly available weights.
 | Qwen2.5 Math | 1.5B, 7B, 72B | Alibaba Group | [An, Yang et al. 2024](https://arxiv.org/abs/2409.12122)                                          |
 | QwQ | 32B | Alibaba Group | [Qwen Team 2024](https://qwenlm.github.io/blog/qwq-32b-preview/)                                                                         |
 | RedPajama-INCITE | 3B, 7B | Together | [Together 2023](https://together.ai/blog/redpajama-models-v1)                                                                 |
+| SmolLM2 | 135M, 360M, 1.7B | Hugging Face | [Hugging Face 2024](https://github.com/huggingface/smollm)                                                               |
 | StableCode | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                                  |
 | Salamandra | 2B, 7B | Barcelona Supercomputing Centre | [BSC-LTC 2024](https://github.com/BSC-LTC/salamandra)                                                                         |
 | StableLM  | 3B, 7B | Stability AI | [Stability AI 2023](https://github.com/Stability-AI/StableLM)                                                                    |
@@ -122,6 +123,12 @@ google/gemma-2b-it
 google/gemma-7b
 google/gemma-7b-it
 h2oai/h2o-danube2-1.8b-chat
+HuggingFaceTB/SmolLM2-135M
+HuggingFaceTB/SmolLM2-135M-Instruct
+HuggingFaceTB/SmolLM2-360M
+HuggingFaceTB/SmolLM2-360M-Instruct
+HuggingFaceTB/SmolLM2-1.7B
+HuggingFaceTB/SmolLM2-1.7B-Instruct
 lmsys/longchat-13b-16k
 lmsys/longchat-7b-16k
 lmsys/vicuna-13b-v1.3