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Model: mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16
Source: Original Platform

.gitattributes

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+
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: mlx
+license: other
+license_name: nvidia-open-model-license
+license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- nvidia
+- llama-3
+- pytorch
+- mlx
+base_model: nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
+datasets:
+- nvidia/Llama-Nemotron-Post-Training-Dataset
+---
+
+# mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16
+
+This model [mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16](https://huggingface.co/mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16) was
+converted to MLX format from [nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1)
+using mlx-lm version **0.25.0**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16")
+
+prompt = "hello"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```

chat_template.jinja

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+{%- if tools %}{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+' -}}{%- if messages[0].role == 'system' and messages[0].content != '' -%}{{- messages[0].content + '
+
+' -}}{%- else -%}{{- 'detailed thinking off
+
+' -}}{%- endif -%}{{- '<AVAILABLE_TOOLS>[' -}}{%- for tool in tools -%}{{- (tool.function if tool.function is defined else tool) | tojson -}}{{- ', ' if not loop.last else '' -}}{%- endfor -%}{{- ']</AVAILABLE_TOOLS>' -}}{{- '<|eot_id|>' -}}{%- else %}{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+' -}}{%- if messages[0].role == 'system' and messages[0].content != '' -%}{{- messages[0].content -}}{%- else -%}{{- 'detailed thinking off' -}}{%- endif %}{{- '<|eot_id|>' -}}{%- endif %}{%- for message in messages -%}{%- if (message.role == 'user') -%}{{- '<|start_header_id|>user<|end_header_id|>
+
+' + message.content + '<|eot_id|>' -}}{%- elif message.role == 'assistant' -%}{%- set content = message.content -%}{{- '<|start_header_id|>assistant<|end_header_id|>
+
+' + content -}}{%- if message.tool_calls -%}{{- '<TOOLCALL>[' -}}{%- for tool_call in message.tool_calls -%}{%- if tool_call.function -%}{%- set tool_call = tool_call.function -%}{%- endif -%}{{- '{"name": "' }}{{- tool_call.name }}{{- '", "arguments": ' -}}{%- if tool_call.arguments is string -%}{{- tool_call.arguments -}}{%- else -%}{{- tool_call.arguments | tojson -}}{%- endif -%}{{- ', ' if not loop.last else '' -}}{%- endfor -%}{{- ']</TOOLCALL>' -}}{%- endif %}{{- '<|eot_id|>' -}}{%- elif message.role == 'tool' -%}{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') -%}{{- '<|start_header_id|>user<|end_header_id|>
+
+' }}{{- '<TOOL_RESPONSE>[' -}}{%- endif -%}{{- message.content -}}{{- ', ' if not loop.last and (messages[loop.index0 + 1].role == 'tool') else '' -}}{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') -%}{{- ']</TOOL_RESPONSE>' -}}{{- '<|eot_id|>' -}}{%- endif %}{%- endif %}{%- endfor %}{%- if add_generation_prompt %}{{- '<|start_header_id|>assistant<|end_header_id|>
+
+' -}}{%- endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "eos_token_id": 128001,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 9216,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "factor": 4.0,
+        "low_freq_factor": 1.0,
+        "high_freq_factor": 4.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 3565775107.2609234,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.47.1",
+    "use_cache": true,
+    "vocab_size": 128256
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "transformers_version": "4.47.1"
+}

llama_nemotron_nano_toolcall_parser.py

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+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import re
+from collections.abc import Sequence
+from typing import Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    DeltaFunctionCall, DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser,
+    ToolParserManager,
+)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("llama_nemotron_json")
+class LlamaNemotronJSONToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        self.tool_call_start_token: str = "<TOOLCALL>"
+        self.tool_call_end_token: str = "</TOOLCALL>"
+
+        self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+
+        else:
+
+            try:
+                str_tool_calls = self.tool_call_regex.findall(model_output)[0].strip()
+                if not str_tool_calls.startswith("["):
+                    str_tool_calls = "[" + str_tool_calls
+                if not str_tool_calls.endswith("]"):
+                    str_tool_calls = "]" + str_tool_calls
+                json_tool_calls = json.loads(str_tool_calls)
+                tool_calls = []
+                for tool_call in json_tool_calls:
+                    try:
+                        tool_calls.append(ToolCall(
+                            type="function",
+                            function=FunctionCall(
+                                name=tool_call["name"],
+                                arguments=json.dumps(tool_call["arguments"], ensure_ascii=False) \
+                                    if isinstance(tool_call["arguments"], dict) else tool_call["arguments"],
+                            ),
+                        ))
+                    except:
+                        continue
+
+                content = model_output[:model_output.rfind(self.tool_call_start_token)]
+
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None,
+                )
+
+            except Exception:
+                logger.exception(f"Error in extracting tool call from response. Response: {model_output}")
+                return ExtractedToolCallInformation(
+                    tools_called=False,
+                    tool_calls=[],
+                    content=model_output,
+                )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        raise NotImplementedError("Tool calling is not supported in streaming mode!")

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