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Model: mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16 Source: Original Platform
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**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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45
README.md
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README.md
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---
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library_name: mlx
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license: other
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license_name: nvidia-open-model-license
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license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- nvidia
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- llama-3
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- pytorch
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- mlx
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base_model: nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
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datasets:
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- nvidia/Llama-Nemotron-Post-Training-Dataset
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---
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# mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16
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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
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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)
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using mlx-lm version **0.25.0**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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' -}}{%- if messages[0].role == 'system' and messages[0].content != '' -%}{{- messages[0].content + '
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' -}}{%- else -%}{{- 'detailed thinking off
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' -}}{%- 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|>
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' -}}{%- 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|>
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' + message.content + '<|eot_id|>' -}}{%- elif message.role == 'assistant' -%}{%- set content = message.content -%}{{- '<|start_header_id|>assistant<|end_header_id|>
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' + 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|>
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' }}{{- '<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|>
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' -}}{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 9216,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 4.0,
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"low_freq_factor": 1.0,
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"high_freq_factor": 4.0,
|
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 3565775107.2609234,
|
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.47.1",
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"use_cache": true,
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"vocab_size": 128256
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}
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
|
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"eos_token_id": [
|
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128001,
|
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128008,
|
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128009
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],
|
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"transformers_version": "4.47.1"
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}
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109
llama_nemotron_nano_toolcall_parser.py
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llama_nemotron_nano_toolcall_parser.py
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# SPDX-License-Identifier: Apache-2.0
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import json
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import re
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from collections.abc import Sequence
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from typing import Union
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import partial_json_parser
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from partial_json_parser.core.options import Allow
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionRequest,
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DeltaFunctionCall, DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall,
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ToolCall,
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)
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from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser,
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ToolParserManager,
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)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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@ToolParserManager.register_module("llama_nemotron_json")
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class LlamaNemotronJSONToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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self.current_tool_name_sent: bool = False
|
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id: int = -1
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self.streamed_args_for_tool: list[str] = []
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|
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self.tool_call_start_token: str = "<TOOLCALL>"
|
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self.tool_call_end_token: str = "</TOOLCALL>"
|
||||
|
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self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
|
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|
||||
def extract_tool_calls(
|
||||
self,
|
||||
model_output: str,
|
||||
request: ChatCompletionRequest,
|
||||
) -> ExtractedToolCallInformation:
|
||||
|
||||
if self.tool_call_start_token not in model_output:
|
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return ExtractedToolCallInformation(
|
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tools_called=False,
|
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tool_calls=[],
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content=model_output,
|
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)
|
||||
|
||||
else:
|
||||
|
||||
try:
|
||||
str_tool_calls = self.tool_call_regex.findall(model_output)[0].strip()
|
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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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3
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|
||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user