初始化项目，由ModelHub XC社区提供模型
Model: LLM4Binary/llm4decompile-22b-v2 Source: Original Platform
This commit is contained in:
ModelHub XC
35
.gitattributes
vendored
Normal file
35
.gitattributes
vendored
Normal file
@@ -0,0 +1,35 @@
|
||||
*.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
|
||||
175
README.md
Normal file
175
README.md
Normal file
@@ -0,0 +1,175 @@
|
||||
---
|
||||
license: mit
|
||||
tags:
|
||||
- decompile
|
||||
- binary
|
||||
---
|
||||
|
||||
### 1. Introduction of LLM4Decompile
|
||||
|
||||
LLM4Decompile aims to decompile x86 assembly instructions into C. The newly released V2 series are trained with a larger dataset (2B tokens) and a maximum token length of 4,096, with remarkable performance (up to 100% improvement) compared to the previous model.
|
||||
|
||||
- **Github Repository:** [LLM4Decompile](https://github.com/albertan017/LLM4Decompile)
|
||||
|
||||
|
||||
### 2. Evaluation Results
|
||||
|
||||
| Metrics | Re-executability Rate | | | | | Edit Similarity | | | | |
|
||||
|:-----------------------:|:---------------------:|:-------:|:-------:|:-------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:-------:|
|
||||
| Optimization Level | O0 | O1 | O2 | O3 | AVG | O0 | O1 | O2 | O3 | AVG |
|
||||
| LLM4Decompile-End-6.7B | 0.6805 | 0.3951 | 0.3671 | 0.3720 | 0.4537 | 0.1557 | 0.1292 | 0.1293 | 0.1269 | 0.1353 |
|
||||
| Ghidra | 0.3476 | 0.1646 | 0.1524 | 0.1402 | 0.2012 | 0.0699 | 0.0613 | 0.0619 | 0.0547 | 0.0620 |
|
||||
| +GPT-4o | 0.4695 | 0.3415 | 0.2866 | 0.3110 | 0.3522 | 0.0660 | 0.0563 | 0.0567 | 0.0499 | 0.0572 |
|
||||
| +LLM4Decompile-Ref-1.3B | 0.6890 | 0.3720 | 0.4085 | 0.3720 | 0.4604 | 0.1517 | 0.1325 | 0.1292 | 0.1267 | 0.1350 |
|
||||
| +LLM4Decompile-Ref-6.7B | 0.7439 | 0.4695 | 0.4756 | 0.4207 | 0.5274 | 0.1559 | 0.1353 | 0.1342 | 0.1273 | 0.1382 |
|
||||
| +LLM4Decompile-Ref-33B | 0.7073 | 0.4756 | 0.4390 | 0.4146 | 0.5091 | 0.1540 | 0.1379 | 0.1363 | 0.1307 | 0.1397 |
|
||||
|
||||
### 3. How to Use
|
||||
Here is an example of how to use our model (Only for V2. For previous models, please check the corresponding model page at HF).
|
||||
|
||||
1. Install Ghidra
|
||||
Download [Ghidra](https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip) to the current folder. You can also check the [page](https://github.com/NationalSecurityAgency/ghidra/releases) for other versions. Unzip the package to the current folder.
|
||||
In bash, you can use the following:
|
||||
```bash
|
||||
cd LLM4Decompile/ghidra
|
||||
wget https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip
|
||||
unzip ghidra_11.0.3_PUBLIC_20240410.zip
|
||||
```
|
||||
2. Install Java-SDK-17
|
||||
Ghidra 11 is dependent on Java-SDK-17, a simple way to install the SDK on Ubuntu:
|
||||
```bash
|
||||
apt-get update
|
||||
apt-get upgrade
|
||||
apt install openjdk-17-jdk openjdk-17-jre
|
||||
```
|
||||
Please check [Ghidra install guide](https://htmlpreview.github.io/?https://github.com/NationalSecurityAgency/ghidra/blob/Ghidra_11.1.1_build/GhidraDocs/InstallationGuide.html) for other platforms.
|
||||
|
||||
3. Use Ghidra Headless to decompile binary (demo.py)
|
||||
|
||||
Note: **Replace** func0 with the function name you want to decompile.
|
||||
|
||||
**Preprocessing:** Compile the C code into binary, and disassemble the binary into assembly instructions.
|
||||
```python
|
||||
import os
|
||||
import subprocess
|
||||
from tqdm import tqdm,trange
|
||||
|
||||
OPT = ["O0", "O1", "O2", "O3"]
|
||||
timeout_duration = 10
|
||||
|
||||
ghidra_path = "./ghidra_11.0.3_PUBLIC/support/analyzeHeadless"#path to the headless analyzer, change the path accordingly
|
||||
postscript = "./decompile.py"#path to the decompiler helper function, change the path accordingly
|
||||
project_path = "."#path to temp folder for analysis, change the path accordingly
|
||||
project_name = "tmp_ghidra_proj"
|
||||
func_path = "../samples/sample.c"#path to c code for compiling and decompiling, change the path accordingly
|
||||
fileName = "sample"
|
||||
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
pid = os.getpid()
|
||||
asm_all = {}
|
||||
for opt in [OPT[0]]:
|
||||
executable_path = os.path.join(temp_dir, f"{pid}_{opt}.o")
|
||||
cmd = f'gcc -{opt} -o {executable_path} {func_path} -lm'
|
||||
subprocess.run(
|
||||
cmd.split(' '),
|
||||
check=True,
|
||||
stdout=subprocess.DEVNULL, # Suppress stdout
|
||||
stderr=subprocess.DEVNULL, # Suppress stderr
|
||||
timeout=timeout_duration,
|
||||
)
|
||||
|
||||
output_path = os.path.join(temp_dir, f"{pid}_{opt}.c")
|
||||
command = [
|
||||
ghidra_path,
|
||||
temp_dir,
|
||||
project_name,
|
||||
"-import", executable_path,
|
||||
"-postScript", postscript, output_path,
|
||||
"-deleteProject", # WARNING: This will delete the project after analysis
|
||||
]
|
||||
result = subprocess.run(command, text=True, capture_output=True, check=True)
|
||||
with open(output_path,'r') as f:
|
||||
c_decompile = f.read()
|
||||
c_func = []
|
||||
flag = 0
|
||||
for line in c_decompile.split('\n'):
|
||||
if "Function: func0" in line:#**Replace** func0 with the function name you want to decompile.
|
||||
flag = 1
|
||||
c_func.append(line)
|
||||
continue
|
||||
if flag:
|
||||
if '// Function:' in line:
|
||||
if len(c_func) > 1:
|
||||
break
|
||||
c_func.append(line)
|
||||
if flag == 0:
|
||||
raise ValueError('bad case no function found')
|
||||
for idx_tmp in range(1,len(c_func)):##########remove the comments
|
||||
if 'func0' in c_func[idx_tmp]:
|
||||
break
|
||||
c_func = c_func[idx_tmp:]
|
||||
input_asm = '\n'.join(c_func).strip()
|
||||
|
||||
before = f"# This is the assembly code:\n"#prompt
|
||||
after = "\n# What is the source code?\n"#prompt
|
||||
input_asm_prompt = before+input_asm.strip()+after
|
||||
with open(fileName +'_' + opt +'.pseudo','w',encoding='utf-8') as f:
|
||||
f.write(input_asm_prompt)
|
||||
```
|
||||
|
||||
Ghidra pseudo-code may look like this:
|
||||
```c
|
||||
undefined4 func0(float param_1,long param_2,int param_3)
|
||||
{
|
||||
int local_28;
|
||||
int local_24;
|
||||
|
||||
local_24 = 0;
|
||||
do {
|
||||
local_28 = local_24;
|
||||
if (param_3 <= local_24) {
|
||||
return 0;
|
||||
}
|
||||
while (local_28 = local_28 + 1, local_28 < param_3) {
|
||||
if ((double)((ulong)(double)(*(float *)(param_2 + (long)local_24 * 4) -
|
||||
*(float *)(param_2 + (long)local_28 * 4)) &
|
||||
SUB168(_DAT_00402010,0)) < (double)param_1) {
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
local_24 = local_24 + 1;
|
||||
} while( true );
|
||||
}
|
||||
```
|
||||
4. Refine pseudo-code using LLM4Decompile (demo.py)
|
||||
|
||||
**Decompilation:** Use LLM4Decompile-Ref to refine the Ghidra pseudo-code into C:
|
||||
```python
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
|
||||
model_path = 'LLM4Binary/llm4decompile-6.7b-v2' # V2 Model
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()
|
||||
|
||||
with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#optimization level O0
|
||||
asm_func = f.read()
|
||||
inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
|
||||
with torch.no_grad():
|
||||
outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
|
||||
c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
|
||||
|
||||
with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#original file
|
||||
func = f.read()
|
||||
|
||||
print(f'pseudo function:\n{func}')# Note we only decompile one function, where the original file may contain multiple functions
|
||||
print(f'refined function:\n{c_func_decompile}')
|
||||
|
||||
```
|
||||
|
||||
### 4. License
|
||||
This code repository is licensed under the MIT License.
|
||||
|
||||
### 5. Contact
|
||||
|
||||
If you have any questions, please raise an issue.
|
||||
25
config.json
Normal file
25
config.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"architectures": [
|
||||
"MistralForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 6144,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 16384,
|
||||
"max_position_embeddings": 32768,
|
||||
"model_type": "mistral",
|
||||
"num_attention_heads": 48,
|
||||
"num_hidden_layers": 56,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.40.2",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32768
|
||||
}
|
||||
290
convert_mistral_weights_to_hf-22B.py
Normal file
290
convert_mistral_weights_to_hf-22B.py
Normal file
@@ -0,0 +1,290 @@
|
||||
# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
import argparse
|
||||
import gc
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import warnings
|
||||
|
||||
import torch
|
||||
from safetensors.torch import load_file as safe_load_file
|
||||
|
||||
from transformers import (
|
||||
LlamaTokenizer,
|
||||
MistralConfig,
|
||||
MistralForCausalLM,
|
||||
)
|
||||
|
||||
|
||||
try:
|
||||
from transformers import LlamaTokenizerFast
|
||||
|
||||
tokenizer_class = LlamaTokenizerFast
|
||||
except ImportError as e:
|
||||
warnings.warn(e)
|
||||
warnings.warn(
|
||||
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
|
||||
)
|
||||
tokenizer_class = LlamaTokenizer
|
||||
|
||||
"""
|
||||
Sample usage:
|
||||
|
||||
```
|
||||
python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
|
||||
--input_dir /path/to/downloaded/mistral/weights --model_size 22B --output_dir /output/path
|
||||
```
|
||||
|
||||
Thereafter, models can be loaded via:
|
||||
|
||||
```py
|
||||
from transformers import MistralForCausalLM, LlamaTokenizer
|
||||
|
||||
model = MistralForCausalLM.from_pretrained("/output/path")
|
||||
tokenizer = LlamaTokenizer.from_pretrained("/output/path")
|
||||
```
|
||||
|
||||
Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
|
||||
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
|
||||
"""
|
||||
|
||||
NUM_SHARDS = {"22B": 1}
|
||||
|
||||
|
||||
def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
|
||||
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
|
||||
|
||||
|
||||
def read_json(path):
|
||||
with open(path, "r") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def write_json(text, path):
|
||||
with open(path, "w") as f:
|
||||
json.dump(text, f)
|
||||
|
||||
|
||||
def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True, is_v3=False):
|
||||
# for backward compatibility, before you needed the repo to be called `my_repo/model_size`
|
||||
if not os.path.isfile(os.path.join(input_base_path, "params.json")):
|
||||
input_base_path = os.path.join(input_base_path, model_size)
|
||||
|
||||
os.makedirs(model_path, exist_ok=True)
|
||||
tmp_model_path = os.path.join(model_path, "tmp")
|
||||
os.makedirs(tmp_model_path, exist_ok=True)
|
||||
|
||||
params = read_json(os.path.join(input_base_path, "params.json"))
|
||||
num_shards = NUM_SHARDS[model_size]
|
||||
|
||||
sliding_window = params.get("sliding_window", None)
|
||||
|
||||
# For some reason this is a string in the params.json
|
||||
if sliding_window is not None:
|
||||
sliding_window = int(sliding_window)
|
||||
|
||||
n_layers = params["n_layers"]
|
||||
n_heads = params["n_heads"]
|
||||
n_heads_per_shard = n_heads // num_shards
|
||||
dim = params["dim"]
|
||||
dims_per_head = dim // n_heads
|
||||
base = params.get("rope_theta", 10000.0)
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
|
||||
max_position_embeddings = 4096 * 8
|
||||
|
||||
if tokenizer_path is not None:
|
||||
tokenizer = tokenizer_class(tokenizer_path + ".v3" if is_v3 else "")
|
||||
tokenizer.save_pretrained(model_path)
|
||||
vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
|
||||
|
||||
if "n_kv_heads" in params:
|
||||
num_key_value_heads = params["n_kv_heads"] # for GQA / MQA
|
||||
num_local_key_value_heads = num_key_value_heads // num_shards
|
||||
key_value_dim = dims_per_head * num_local_key_value_heads
|
||||
else: # compatibility with other checkpoints
|
||||
num_key_value_heads = n_heads
|
||||
num_local_key_value_heads = n_heads_per_shard
|
||||
key_value_dim = dim
|
||||
|
||||
# permute for sliced rotary
|
||||
def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
|
||||
return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
|
||||
|
||||
print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
|
||||
|
||||
# Load weights - for v3 models the consolidated weights are in a single file format in safetensors
|
||||
if is_v3:
|
||||
loaded = [safe_load_file(os.path.join(input_base_path, "consolidated.safetensors"))]
|
||||
else:
|
||||
loaded = [
|
||||
torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
|
||||
for i in range(num_shards)
|
||||
]
|
||||
param_count = 0
|
||||
index_dict = {"weight_map": {}}
|
||||
for layer_i in range(n_layers):
|
||||
filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
|
||||
|
||||
# Sharded
|
||||
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
|
||||
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
|
||||
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
|
||||
|
||||
state_dict = {
|
||||
f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
|
||||
f"layers.{layer_i}.attention_norm.weight"
|
||||
].clone(),
|
||||
f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
|
||||
f"layers.{layer_i}.ffn_norm.weight"
|
||||
].clone(),
|
||||
}
|
||||
state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
|
||||
torch.cat(
|
||||
[
|
||||
loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
|
||||
for i in range(num_shards)
|
||||
],
|
||||
dim=0,
|
||||
).reshape(dim, dim)
|
||||
)
|
||||
state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
|
||||
torch.cat(
|
||||
[
|
||||
loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
|
||||
num_local_key_value_heads, dims_per_head, dim
|
||||
)
|
||||
for i in range(num_shards)
|
||||
],
|
||||
dim=0,
|
||||
).reshape(key_value_dim, dim),
|
||||
num_key_value_heads,
|
||||
key_value_dim,
|
||||
dim,
|
||||
)
|
||||
state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
|
||||
[
|
||||
loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_local_key_value_heads, dims_per_head, dim)
|
||||
for i in range(num_shards)
|
||||
],
|
||||
dim=0,
|
||||
).reshape(key_value_dim, dim)
|
||||
|
||||
state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
|
||||
[loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
|
||||
)
|
||||
state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
|
||||
[loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
|
||||
)
|
||||
state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
|
||||
[loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
|
||||
)
|
||||
state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
|
||||
[loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
|
||||
)
|
||||
|
||||
state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
|
||||
for k, v in state_dict.items():
|
||||
index_dict["weight_map"][k] = filename
|
||||
param_count += v.numel()
|
||||
torch.save(state_dict, os.path.join(tmp_model_path, filename))
|
||||
|
||||
filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
|
||||
state_dict = {
|
||||
"model.norm.weight": loaded[0]["norm.weight"],
|
||||
"model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1),
|
||||
"lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
|
||||
}
|
||||
|
||||
for k, v in state_dict.items():
|
||||
index_dict["weight_map"][k] = filename
|
||||
param_count += v.numel()
|
||||
torch.save(state_dict, os.path.join(tmp_model_path, filename))
|
||||
|
||||
# Write configs
|
||||
index_dict["metadata"] = {"total_size": param_count * 2}
|
||||
write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
|
||||
config = MistralConfig(
|
||||
hidden_size=dim,
|
||||
intermediate_size=params["hidden_dim"],
|
||||
num_attention_heads=params["n_heads"],
|
||||
num_hidden_layers=params["n_layers"],
|
||||
rms_norm_eps=params["norm_eps"],
|
||||
num_key_value_heads=num_key_value_heads,
|
||||
vocab_size=vocab_size,
|
||||
rope_theta=base,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
sliding_window=sliding_window,
|
||||
)
|
||||
config.save_pretrained(tmp_model_path)
|
||||
|
||||
# Make space so we can load the model properly now.
|
||||
del state_dict
|
||||
del loaded
|
||||
gc.collect()
|
||||
|
||||
print("Loading the checkpoint in a Mistral model.")
|
||||
model = MistralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
|
||||
# Avoid saving this as part of the config.
|
||||
del model.config._name_or_path
|
||||
model.config.torch_dtype = torch.float16
|
||||
print("Saving in the Transformers format.")
|
||||
|
||||
model.save_pretrained(model_path, safe_serialization=safe_serialization)
|
||||
shutil.rmtree(tmp_model_path)
|
||||
|
||||
|
||||
def write_tokenizer(tokenizer_path, input_tokenizer_path):
|
||||
# Initialize the tokenizer based on the `spm` model
|
||||
print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
|
||||
tokenizer = tokenizer_class(input_tokenizer_path)
|
||||
tokenizer.save_pretrained(tokenizer_path)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--input_dir",
|
||||
help="Location of Mistral weights, which contains tokenizer.model and model folders",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_size",
|
||||
choices=["22B", "tokenizer_only"],
|
||||
help="'f' models correspond to the finetuned versions, and are specific to the Mistral2 official release. For more details on Mistral2, checkout the original repo: https://huggingface.co/meta-mistral",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
help="Location to write HF model and tokenizer",
|
||||
)
|
||||
parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
|
||||
parser.add_argument(
|
||||
"--is_v3", action="store_true", help="Whether the checkpoints correspond to the 3rd version or not."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
spm_path = os.path.join(args.input_dir, "tokenizer.model")
|
||||
if args.model_size != "tokenizer_only":
|
||||
write_model(
|
||||
model_path=args.output_dir,
|
||||
input_base_path=args.input_dir,
|
||||
model_size=args.model_size,
|
||||
safe_serialization=args.safe_serialization,
|
||||
tokenizer_path=spm_path,
|
||||
is_v3=args.is_v3,
|
||||
)
|
||||
else:
|
||||
write_tokenizer(args.output_dir, spm_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.40.2"
|
||||
}
|
||||
3
model-00001-of-00009.safetensors
Normal file
3
model-00001-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:042e004e6b96590b58ad96cf7e894b5885b901ddc9473ecad278c6a046fb2f9a
|
||||
size 4882298776
|
||||
3
model-00002-of-00009.safetensors
Normal file
3
model-00002-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7dddc146e11aece252c6e632c8d745cb6fcce391dca97999df21e555f260091a
|
||||
size 4983012160
|
||||
3
model-00003-of-00009.safetensors
Normal file
3
model-00003-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:90ef3a481570204d0269dbb057e7c114943fb7183043fedbfe82c60ea05631e2
|
||||
size 4957821336
|
||||
3
model-00004-of-00009.safetensors
Normal file
3
model-00004-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:60303704557eac02c2b6f7ad8f3696ae5e6d5dfa62ee3f578e1946e495892e82
|
||||
size 4882323744
|
||||
3
model-00005-of-00009.safetensors
Normal file
3
model-00005-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e3a955359951b07bda59d17310860c1749255bd0c4f04ff985180e0b25df33d0
|
||||
size 4983012192
|
||||
3
model-00006-of-00009.safetensors
Normal file
3
model-00006-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4ce0365da717f6b313ce5c1b84f6a43ee9ae33b93368f3a25d11f0c9ec876ea0
|
||||
size 4957821336
|
||||
3
model-00007-of-00009.safetensors
Normal file
3
model-00007-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:28fd5c9f3e2193a7cec5ceffcf205c698c02a2e59f9435099507febe878f3142
|
||||
size 4882323744
|
||||
3
model-00008-of-00009.safetensors
Normal file
3
model-00008-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5339bdc8151876346906037c5ac5ec612e12432744201694a2520f1eb4f33dc2
|
||||
size 4983012192
|
||||
3
model-00009-of-00009.safetensors
Normal file
3
model-00009-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61d3880f6227dbcd4849152a2ed6e1774f1412e6c0f8afe42e9980ebed6ecb29
|
||||
size 4982999056
|
||||
514
model.safetensors.index.json
Normal file
514
model.safetensors.index.json
Normal file
@@ -0,0 +1,514 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 44494565376
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00009-of-00009.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.12.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.12.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.13.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.input_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.mlp.down_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.mlp.up_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.post_attention_layernorm.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.18.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.18.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.18.mlp.gate_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.18.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.18.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.18.self_attn.o_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.weight": "model-00003-of-00009.safetensors",
|
||||
"model.layers.19.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.2.input_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.20.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.input_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.mlp.down_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.post_attention_layernorm.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.24.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.24.mlp.gate_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.mlp.up_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.24.self_attn.k_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.self_attn.o_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.self_attn.q_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.24.self_attn.v_proj.weight": "model-00004-of-00009.safetensors",
|
||||
"model.layers.25.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.25.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.26.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.27.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.3.input_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.30.input_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.mlp.down_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.mlp.gate_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.mlp.up_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.post_attention_layernorm.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.self_attn.o_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.30.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.31.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.self_attn.k_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.31.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.31.self_attn.q_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.31.self_attn.v_proj.weight": "model-00005-of-00009.safetensors",
|
||||
"model.layers.32.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.32.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.33.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.34.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.35.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.input_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.mlp.down_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.mlp.up_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.post_attention_layernorm.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.36.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.37.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.37.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.37.mlp.gate_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.37.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.37.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.37.self_attn.k_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.37.self_attn.o_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.37.self_attn.q_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.37.self_attn.v_proj.weight": "model-00006-of-00009.safetensors",
|
||||
"model.layers.38.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.38.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.39.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.4.input_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.40.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.40.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.41.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.input_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.mlp.down_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.post_attention_layernorm.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.42.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.43.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.43.mlp.gate_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.mlp.up_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.43.self_attn.k_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.self_attn.o_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.self_attn.q_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.43.self_attn.v_proj.weight": "model-00007-of-00009.safetensors",
|
||||
"model.layers.44.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.44.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.45.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.46.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.47.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.48.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.input_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.mlp.down_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.mlp.gate_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.mlp.up_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.post_attention_layernorm.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.self_attn.o_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.49.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.5.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.5.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.5.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
|
||||
"model.layers.50.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.self_attn.k_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.50.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.50.self_attn.q_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.50.self_attn.v_proj.weight": "model-00008-of-00009.safetensors",
|
||||
"model.layers.51.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.self_attn.k_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.self_attn.q_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.51.self_attn.v_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.self_attn.k_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.self_attn.q_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.52.self_attn.v_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.self_attn.k_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.self_attn.q_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.53.self_attn.v_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.self_attn.k_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.self_attn.q_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.54.self_attn.v_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.input_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.mlp.down_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.mlp.gate_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.mlp.up_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.post_attention_layernorm.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.self_attn.k_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.self_attn.o_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.self_attn.q_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.55.self_attn.v_proj.weight": "model-00009-of-00009.safetensors",
|
||||
"model.layers.6.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00009.safetensors",
|
||||
"model.norm.weight": "model-00009-of-00009.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"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
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
91945
tokenizer.json
Normal file
91945
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9addc8bdce5988448ae81b729336f43a81262160ae8da760674badab9d4c7d33
|
||||
size 587591
|
||||
90
tokenizer_config.json
Normal file
90
tokenizer_config.json
Normal file
@@ -0,0 +1,90 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": true,
|
||||
"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
|
||||
},
|
||||
"3": {
|
||||
"content": "[INST]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"4": {
|
||||
"content": "[/INST]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"10": {
|
||||
"content": "[IMG]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"11": {
|
||||
"content": "[PREFIX]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"12": {
|
||||
"content": "[MIDDLE]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"13": {
|
||||
"content": "[SUFFIX]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user