Adding `safetensors` variant of this model
#4
by
aisltnab
- opened
- LICENSE.txt +110 -25
- README.md +8 -40
- langdemo.py +0 -148
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +370 -0
LICENSE.txt
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1. License Rights and Redistribution.
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Nexusflow’s intellectual property or other rights owned by Nexusflow embodied in the Nexusflow Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Nexusflow Materials.
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b. Redistribution and Use.
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i. If you distribute or make the Nexusflow Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party.
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ii. If you receive Nexusflow Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 1 of this Agreement will not apply to you.
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iii. You must retain in all copies of the Nexusflow Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “NexusRaven-V2 is licensed under the Nexusflow License, Copyright © Nexusflow.ai Inc. All Rights Reserved.”
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iv. Your use of the Nexusflow Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to Nexusflow terms and policies (if any), which are hereby incorporated by reference into this Agreement. The Nexusflow Materials are derived from Llama 2 as offered by Meta Platforms Ireland Limited or Meta Platforms, Inc., and you further agree that your use of the Nexusflow Materials shall be subject to the applicable terms and conditions of the Llama 2 Community License Agreement, available at https://ai.meta.com/llama/license/.
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v. You will not use the Nexusflow Materials or any output or results of the Nexusflow Materials to improve any other large language model (excluding NexusRaven-V2 or derivative works thereof).
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5. Intellectual Property.
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a. No trademark licenses are granted under this Agreement, and in connection with the Nexusflow Materials, neither Nexusflow nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and using the Nexusflow Materials.
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b. Subject to Nexusflow’s ownership of Nexusflow Materials and derivatives made by or for Nexusflow (and any rights retained therein by its licensors to the foregoing), with respect to any derivative works and modifications of the Nexusflow Materials that are made by you, as between you and Nexusflow, you are and will be the owner of such derivative works and modifications.
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c. You will indemnify and hold harmless Nexusflow from and against any claim by any third party arising out of or related to your use of the Nexusflow Materials.
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7. Governing Law and Jurisdiction. This Agreement will be governed and
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LLAMA 2 COMMUNITY LICENSE AGREEMENT
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Llama 2 Version Release Date: July 18, 2023
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accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
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construed under the laws of the State of California without regard to choice of law
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does not apply to this Agreement. The courts of California shall have exclusive
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README.md
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---
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license:
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base_model: codellama/CodeLlama-13b-Instruct-hf
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model-index:
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- name: NexusRaven-13B
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results: []
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tags:
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- function calling
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---
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# NexusRaven-13B: Surpassing GPT-4 for Zero-shot Function Calling
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<p align="center">
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## NexusRaven-V2 model usage
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These python functions can do anything (including sending GET/POST requests to external APIs!).
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The two requirements include the python function signature and the appropriate docstring to generate the function call.
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NexusRaven-V2 also does best on functions with arguments, so please always only provide functions that require arguments to raven.
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### NexusRaven-V2's Capabilities
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### Quick Start Prompting Guide
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Please refer to our notebook, [How-To-Prompt.ipynb](
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1. When giving docstrings to Raven, please provide well-indented, detailed, and well-written docstrings as this can help accuracy.
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2. Raven does better when all functions provided to it has arguments, either required or optional, (i.e. ```func(dummy_arg)``` is preferred over ```func()```) as this can help accuracy.
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3. We strongly recommend to set sampling to False when prompting NexusRaven-V2.
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4. We strongly recommend a very low temperature (~0.001).
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5. We strongly recommend following the prompting style below.
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When handling irrelevant user queries, users have noticed that specifying a "no-op" function with arguments work best. For example, something like this might work:
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"""
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Call this when no other provided function can be called to answer the user query.
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"""
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```
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Please ensure to provide an argument to this function, as Raven works best on functions with arguments.
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```
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This can be added above the User Query to "allow" the model to use parallel calls, otherwise, the model will focus on nested and single calls primarily.
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### Quickstart
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You can run the model on a GPU using the following code.
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[If you currently have a workflow that is built around OpenAI's function calling and you want to try NexusRaven-V2, we have a package that helps you drop in NexusRaven-V2.](https://github.com/nexusflowai/nexusraven-pip)
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### Using With LangChain
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We've also included a [small demo for using Raven with langchain](langdemo.py)!
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## Evaluation
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3. The explanations generated by NexusRaven-V2 might be incorrect. Please ensure proper guardrails are present to capture errant behavior.
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## License
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This model was trained on commercially viable data and is licensed under the [
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## References
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```
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## Contact
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Please join our [Discord Channel](https://discord.gg/HDSVmNAs3y) to reach out for any issues and comments!
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---
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license: llama2
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base_model: codellama/CodeLlama-13b-Instruct-hf
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model-index:
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- name: NexusRaven-13B
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results: []
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---
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# NexusRaven-13B: Surpassing GPT-4 for Zero-shot Function Calling
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<p align="center">
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## NexusRaven-V2 model usage
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NexusRaven-V2 accepts a list of python functions. These python functions can do anything (including sending GET/POST requests to external APIs!). The two requirements include the python function signature and the appropriate docstring to generate the function call.
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### NexusRaven-V2's Capabilities
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### Quick Start Prompting Guide
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Please refer to our notebook, [How-To-Prompt.ipynb](How-To-Prompt.ipynb), for more advanced tutorials on using NexusRaven-V2!
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1. We strongly recommend to set sampling to False when prompting NexusRaven-V2.
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2. We strongly recommend a very low temperature (~0.001).
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3. We strongly recommend following the prompting style below.
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### Quickstart
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You can run the model on a GPU using the following code.
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[If you currently have a workflow that is built around OpenAI's function calling and you want to try NexusRaven-V2, we have a package that helps you drop in NexusRaven-V2.](https://github.com/nexusflowai/nexusraven-pip)
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## Evaluation
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3. The explanations generated by NexusRaven-V2 might be incorrect. Please ensure proper guardrails are present to capture errant behavior.
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## License
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This model was trained on commercially viable data and is licensed under the [Llama 2 community license](https://huggingface.co/codellama/CodeLlama-13b-hf/blob/main/LICENSE) following the original [CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf/) model.
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## References
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```
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## Contact
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Please join our [Discord Channel](https://discord.gg/HDSVmNAs3y) to reach out for any issues and comments!
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langdemo.py
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import math
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| 4 |
-
|
| 5 |
-
from langchain.tools.base import StructuredTool
|
| 6 |
-
from langchain.agents import (
|
| 7 |
-
Tool,
|
| 8 |
-
AgentExecutor,
|
| 9 |
-
LLMSingleActionAgent,
|
| 10 |
-
AgentOutputParser,
|
| 11 |
-
)
|
| 12 |
-
from langchain.schema import AgentAction, AgentFinish, OutputParserException
|
| 13 |
-
from langchain.prompts import StringPromptTemplate
|
| 14 |
-
from langchain.llms import HuggingFaceTextGenInference
|
| 15 |
-
from langchain.chains import LLMChain
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
##########################################################
|
| 19 |
-
# Step 1: Define the functions you want to articulate. ###
|
| 20 |
-
##########################################################
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def calculator(
|
| 24 |
-
input_a: float,
|
| 25 |
-
input_b: float,
|
| 26 |
-
operation: Literal["add", "subtract", "multiply", "divide"],
|
| 27 |
-
):
|
| 28 |
-
"""
|
| 29 |
-
Computes a calculation.
|
| 30 |
-
|
| 31 |
-
Args:
|
| 32 |
-
input_a (float) : Required. The first input.
|
| 33 |
-
input_b (float) : Required. The second input.
|
| 34 |
-
operation (string): The operation. Choices include: add to add two numbers, subtract to subtract two numbers, multiply to multiply two numbers, and divide to divide them.
|
| 35 |
-
"""
|
| 36 |
-
match operation:
|
| 37 |
-
case "add":
|
| 38 |
-
return input_a + input_b
|
| 39 |
-
case "subtract":
|
| 40 |
-
return input_a - input_b
|
| 41 |
-
case "multiply":
|
| 42 |
-
return input_a * input_b
|
| 43 |
-
case "divide":
|
| 44 |
-
return input_a / input_b
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
def cylinder_volume(radius, height):
|
| 48 |
-
"""
|
| 49 |
-
Calculate the volume of a cylinder.
|
| 50 |
-
|
| 51 |
-
Parameters:
|
| 52 |
-
- radius (float): The radius of the base of the cylinder.
|
| 53 |
-
- height (float): The height of the cylinder.
|
| 54 |
-
|
| 55 |
-
Returns:
|
| 56 |
-
- float: The volume of the cylinder.
|
| 57 |
-
"""
|
| 58 |
-
if radius < 0 or height < 0:
|
| 59 |
-
raise ValueError("Radius and height must be non-negative.")
|
| 60 |
-
|
| 61 |
-
volume = math.pi * (radius**2) * height
|
| 62 |
-
return volume
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
#############################################################
|
| 66 |
-
# Step 2: Let's define some utils for building the prompt ###
|
| 67 |
-
#############################################################
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
RAVEN_PROMPT = """
|
| 71 |
-
{raven_tools}
|
| 72 |
-
User Query: {input}<human_end>
|
| 73 |
-
|
| 74 |
-
"""
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
# Set up a prompt template
|
| 79 |
-
class RavenPromptTemplate(StringPromptTemplate):
|
| 80 |
-
# The template to use
|
| 81 |
-
template: str
|
| 82 |
-
# The list of tools available
|
| 83 |
-
tools: List[Tool]
|
| 84 |
-
|
| 85 |
-
def format(self, **kwargs) -> str:
|
| 86 |
-
prompt = ""
|
| 87 |
-
for tool in self.tools:
|
| 88 |
-
func_signature, func_docstring = tool.description.split(" - ", 1)
|
| 89 |
-
prompt += f'\nFunction:\ndef {func_signature}\n"""\n{func_docstring}\n"""\n'
|
| 90 |
-
kwargs["raven_tools"] = prompt
|
| 91 |
-
return self.template.format(**kwargs).replace("{{", "{").replace("}}", "}")
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
class RavenOutputParser(AgentOutputParser):
|
| 95 |
-
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
|
| 96 |
-
# Check if agent should finish
|
| 97 |
-
if "Call:" in llm_output:
|
| 98 |
-
return AgentFinish(
|
| 99 |
-
return_values={
|
| 100 |
-
"output": llm_output.strip()
|
| 101 |
-
.replace("Call:", "")
|
| 102 |
-
.strip()
|
| 103 |
-
},
|
| 104 |
-
log=llm_output,
|
| 105 |
-
)
|
| 106 |
-
else:
|
| 107 |
-
raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
##################################################
|
| 111 |
-
# Step 3: Build the agent with these utilities ###
|
| 112 |
-
##################################################
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
inference_server_url = "https://rjmy54al17scvxjr.us-east-1.aws.endpoints.huggingface.cloud"
|
| 116 |
-
assert (
|
| 117 |
-
inference_server_url is not "<YOUR ENDPOINT URL>"
|
| 118 |
-
), "Please provide your own HF inference endpoint URL!"
|
| 119 |
-
|
| 120 |
-
llm = HuggingFaceTextGenInference(
|
| 121 |
-
inference_server_url=inference_server_url,
|
| 122 |
-
temperature=0.001,
|
| 123 |
-
max_new_tokens=400,
|
| 124 |
-
do_sample=False,
|
| 125 |
-
)
|
| 126 |
-
tools = [
|
| 127 |
-
StructuredTool.from_function(calculator),
|
| 128 |
-
StructuredTool.from_function(cylinder_volume),
|
| 129 |
-
]
|
| 130 |
-
raven_prompt = RavenPromptTemplate(
|
| 131 |
-
template=RAVEN_PROMPT, tools=tools, input_variables=["input"]
|
| 132 |
-
)
|
| 133 |
-
llm_chain = LLMChain(llm=llm, prompt=raven_prompt)
|
| 134 |
-
output_parser = RavenOutputParser()
|
| 135 |
-
agent = LLMSingleActionAgent(
|
| 136 |
-
llm_chain=llm_chain,
|
| 137 |
-
output_parser=output_parser,
|
| 138 |
-
stop=["<bot_end>"],
|
| 139 |
-
allowed_tools=tools,
|
| 140 |
-
)
|
| 141 |
-
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
|
| 142 |
-
|
| 143 |
-
call = agent_chain.run(
|
| 144 |
-
"I have a cake that is about 3 centimenters high and 200 centimeters in radius. How much cake do I have?"
|
| 145 |
-
)
|
| 146 |
-
print(eval(call))
|
| 147 |
-
call = agent_chain.run("What is 1+10?")
|
| 148 |
-
print(eval(call))
|
|
|
|
|
|
|
|
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|
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|
|
model-00001-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:14c0ff1fa640063c6084b6513fe35122dc5625f29b9af8317ee2c0a8444c7216
|
| 3 |
+
size 9948933792
|
model-00002-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1f995c96612e274a416caa74e11718a5e7a514023357d93b24139e36e91fe8d0
|
| 3 |
+
size 9904123752
|
model-00003-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f94fd7c010d2b7c0c801c3dd94cd3fadbe626c2076bec0cf6e3b465a41053867
|
| 3 |
+
size 6179204888
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,370 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 26032220160
|
| 4 |
+
},
|
| 5 |
+
"weight_map": {
|
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| 369 |
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| 370 |
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}
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