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environment.yml ADDED
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1
+ name: aspdac24
2
+ channels:
3
+ - dglteam/label/cu117
4
+ - conda-forge
5
+ - defaults
6
+ dependencies:
7
+ - _libgcc_mutex=0.1=conda_forge
8
+ - _openmp_mutex=4.5=2_gnu
9
+ - at-spi2-atk=2.38.0=h0630a04_3
10
+ - at-spi2-core=2.40.3=h0630a04_0
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+ - atk-1.0=2.38.0=hd4edc92_1
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+ - brotli=1.1.0=hd590300_1
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+ - brotli-bin=1.1.0=hd590300_1
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+ - brotli-python=1.0.9=py311h6a678d5_7
15
+ - bzip2=1.0.8=h7b6447c_0
16
+ - ca-certificates=2023.12.12=h06a4308_0
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+ - cairo=1.16.0=hb05425b_5
18
+ - cairomm-1.16=1.16.2=h7e731d7_1
19
+ - certifi=2023.11.17=py311h06a4308_0
20
+ - cffi=1.16.0=py311hb3a22ac_0
21
+ - contourpy=1.2.0=py311h9547e67_0
22
+ - cryptography=41.0.7=py311hdda0065_0
23
+ - cycler=0.12.1=pyhd8ed1ab_0
24
+ - dbus=1.13.6=h5008d03_3
25
+ - dgl=1.1.2.cu117=py311_0
26
+ - epoxy=1.5.10=h166bdaf_1
27
+ - expat=2.5.0=hcb278e6_1
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+ - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
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+ - font-ttf-inconsolata=3.000=h77eed37_0
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+ - font-ttf-source-code-pro=2.038=h77eed37_0
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+ - font-ttf-ubuntu=0.83=h77eed37_1
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+ - fontconfig=2.14.1=h4c34cd2_2
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+ - fonts-conda-ecosystem=1=0
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+ - fonts-conda-forge=1=0
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+ - fonttools=4.46.0=py311h459d7ec_0
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+ - freetype=2.12.1=h267a509_2
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+ - fribidi=1.0.10=h36c2ea0_0
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+ - gdk-pixbuf=2.42.10=h6c15284_3
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+ - gettext=0.21.1=h27087fc_0
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+ - glib=2.78.3=hfc55251_0
41
+ - glib-tools=2.78.3=hfc55251_0
42
+ - gmp=6.3.0=h59595ed_0
43
+ - graph-tool=2.58=py311h35b2f40_2
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+ - graph-tool-base=2.58=py311h2a117df_2
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+ - graphite2=1.3.14=h295c915_1
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+ - gtk3=3.24.34=h13ada96_0
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+ - harfbuzz=4.3.0=hf52aaf7_2
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+ - hicolor-icon-theme=0.17=ha770c72_2
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+ - icu=73.2=h59595ed_0
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+ - keyutils=1.6.1=h166bdaf_0
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+ - kiwisolver=1.4.5=py311h9547e67_1
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+ - krb5=1.21.2=h659d440_0
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+ - lcms2=2.15=h7f713cb_2
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+ - ld_impl_linux-64=2.38=h1181459_1
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+ - lerc=4.0.0=h27087fc_0
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+ - libblas=3.9.0=20_linux64_openblas
57
+ - libboost=1.82.0=h6fcfa73_6
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+ - libboost-python=1.82.0=py311h92ebd52_6
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+ - libbrotlicommon=1.1.0=hd590300_1
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+ - libbrotlidec=1.1.0=hd590300_1
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+ - libbrotlienc=1.1.0=hd590300_1
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+ - libcblas=3.9.0=20_linux64_openblas
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+ - libcups=2.3.3=h4637d8d_4
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+ - libdeflate=1.19=hd590300_0
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+ - libedit=3.1.20191231=he28a2e2_2
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+ - libexpat=2.5.0=hcb278e6_1
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+ - libffi=3.4.4=h6a678d5_0
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+ - libgcc-ng=13.2.0=h807b86a_3
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+ - libgfortran-ng=13.2.0=h69a702a_3
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+ - libgfortran5=13.2.0=ha4646dd_3
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+ - libgirepository=1.78.1=h003a4f0_1
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+ - libgomp=13.2.0=h807b86a_3
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+ - libiconv=1.17=hd590300_1
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+ - libjpeg-turbo=2.1.5.1=hd590300_1
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+ - liblapack=3.9.0=20_linux64_openblas
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+ - libopenblas=0.3.25=pthreads_h413a1c8_0
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+ - libpng=1.6.39=h753d276_0
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+ - librsvg=2.54.4=h7abd40a_0
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+ - libstdcxx-ng=13.2.0=h7e041cc_3
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+ - libtiff=4.6.0=h29866fb_1
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+ - libuuid=1.41.5=h5eee18b_0
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+ - libwebp-base=1.3.2=hd590300_0
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+ - libxcb=1.15=h0b41bf4_0
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+ - libxml2=2.10.4=hf1b16e4_1
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+ - libzlib=1.2.13=hd590300_5
87
+ - matplotlib-base=3.8.0=py311h54ef318_1
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+ - munkres=1.1.4=pyh9f0ad1d_0
89
+ - ncurses=6.4=h6a678d5_0
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+ - numpy=1.26.2=py311h64a7726_0
91
+ - openjpeg=2.5.0=h488ebb8_3
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+ - openssl=3.2.0=hd590300_1
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+ - packaging=23.2=pyhd8ed1ab_0
94
+ - pango=1.50.7=hbd2fdc8_0
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+ - pcre2=10.42=hcad00b1_0
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+ - pillow=10.0.1=py311h8aef010_1
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+ - pip=23.3.1=py311h06a4308_0
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+ - pixman=0.42.2=h59595ed_0
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+ - pthread-stubs=0.4=h36c2ea0_1001
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+ - pycairo=1.25.0=py311h8feb60e_1
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+ - pycparser=2.21=pyhd8ed1ab_0
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+ - pygobject=3.46.0=py311h86ed371_1
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+ - pyopenssl=23.2.0=py311h06a4308_0
104
+ - pyparsing=3.1.1=pyhd8ed1ab_0
105
+ - pysocks=1.7.1=py311h06a4308_0
106
+ - python=3.11.5=h955ad1f_0
107
+ - python-dateutil=2.8.2=pyhd8ed1ab_0
108
+ - python_abi=3.11=2_cp311
109
+ - readline=8.2=h5eee18b_0
110
+ - requests=2.31.0=py311h06a4308_0
111
+ - scipy=1.11.4=py311h64a7726_0
112
+ - setuptools=68.2.2=py311h06a4308_0
113
+ - sigcpp-3.0=3.6.0=h59595ed_0
114
+ - six=1.16.0=pyh6c4a22f_0
115
+ - sparsehash=2.0.4=hcb278e6_1
116
+ - sqlite=3.41.2=h5eee18b_0
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+ - tk=8.6.12=h1ccaba5_0
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+ - wheel=0.41.2=py311h06a4308_0
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+ - xorg-fixesproto=5.0=h7f98852_1002
120
+ - xorg-inputproto=2.3.2=h7f98852_1002
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+ - xorg-kbproto=1.0.7=h7f98852_1002
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+ - xorg-libx11=1.8.7=h8ee46fc_0
123
+ - xorg-libxau=1.0.11=hd590300_0
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+ - xorg-libxdmcp=1.1.3=h7f98852_0
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+ - xorg-libxext=1.3.4=h0b41bf4_2
126
+ - xorg-libxfixes=5.0.3=h7f98852_1004
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+ - xorg-libxi=1.7.10=h7f98852_0
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+ - xorg-libxtst=1.2.3=h7f98852_1002
129
+ - xorg-recordproto=1.14.2=h7f98852_1002
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+ - xorg-xextproto=7.3.0=h0b41bf4_1003
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+ - xorg-xproto=7.0.31=h7f98852_1007
132
+ - xz=5.4.5=h5eee18b_0
133
+ - zlib=1.2.13=hd590300_5
134
+ - zstandard=0.22.0=py311haa97af0_0
135
+ - zstd=1.5.5=hfc55251_0
136
+ - pip:
137
+ - accelerate==0.27.2
138
+ - aiohttp==3.9.3
139
+ - aiosignal==1.3.1
140
+ - annotated-types==0.6.0
141
+ - anyio==4.3.0
142
+ - attrs==23.1.0
143
+ - beautifulsoup4==4.12.2
144
+ - bitsandbytes==0.42.0
145
+ - bleach==6.1.0
146
+ - chardet==5.2.0
147
+ - charset-normalizer==3.3.2
148
+ - datasets==2.16.1
149
+ - deepspeed==0.13.4
150
+ - defusedxml==0.7.1
151
+ - dill==0.3.7
152
+ - distro==1.9.0
153
+ - docstring-parser==0.15
154
+ - einops==0.7.0
155
+ - et-xmlfile==1.1.0
156
+ - fairscale==0.4.13
157
+ - fastjsonschema==2.19.1
158
+ - filelock==3.13.1
159
+ - fire==0.5.0
160
+ - flash-attn==2.5.5
161
+ - frozenlist==1.4.1
162
+ - fsspec==2023.10.0
163
+ - h11==0.14.0
164
+ - hjson==3.1.0
165
+ - httpcore==1.0.4
166
+ - httpx==0.27.0
167
+ - huggingface-hub==0.20.3
168
+ - idna==3.6
169
+ - jinja2==3.1.2
170
+ - joblib==1.3.2
171
+ - jsonlines==4.0.0
172
+ - jsonschema==4.20.0
173
+ - jsonschema-specifications==2023.12.1
174
+ - jupyter-client==8.6.0
175
+ - jupyter-core==5.5.1
176
+ - jupyterlab-pygments==0.3.0
177
+ - markdown-it-py==3.0.0
178
+ - markupsafe==2.1.3
179
+ - mdurl==0.1.2
180
+ - mistune==3.0.2
181
+ - mpmath==1.3.0
182
+ - multidict==6.0.5
183
+ - multiprocess==0.70.15
184
+ - nbclient==0.9.0
185
+ - nbconvert==7.13.1
186
+ - nbformat==5.9.2
187
+ - networkx==3.2.1
188
+ - ninja==1.11.1.1
189
+ - nvidia-cublas-cu11==11.10.3.66
190
+ - nvidia-cublas-cu12==12.1.3.1
191
+ - nvidia-cuda-cupti-cu12==12.1.105
192
+ - nvidia-cuda-nvrtc-cu11==11.7.99
193
+ - nvidia-cuda-nvrtc-cu12==12.1.105
194
+ - nvidia-cuda-runtime-cu11==11.7.99
195
+ - nvidia-cuda-runtime-cu12==12.1.105
196
+ - nvidia-cudnn-cu11==8.5.0.96
197
+ - nvidia-cudnn-cu12==8.9.2.26
198
+ - nvidia-cufft-cu12==11.0.2.54
199
+ - nvidia-curand-cu12==10.3.2.106
200
+ - nvidia-cusolver-cu12==11.4.5.107
201
+ - nvidia-cusparse-cu12==12.1.0.106
202
+ - nvidia-nccl-cu12==2.19.3
203
+ - nvidia-nvjitlink-cu12==12.3.101
204
+ - nvidia-nvtx-cu12==12.1.105
205
+ - openai==1.13.3
206
+ - openpyxl==3.1.2
207
+ - pandas==2.1.4
208
+ - pandocfilters==1.5.0
209
+ - peft==0.8.2
210
+ - platformdirs==4.1.0
211
+ - protobuf==4.25.2
212
+ - psutil==5.9.7
213
+ - py-cpuinfo==9.0.0
214
+ - pyarrow==15.0.0
215
+ - pyarrow-hotfix==0.6
216
+ - pydantic==2.6.2
217
+ - pydantic-core==2.16.3
218
+ - pygments==2.17.2
219
+ - pynvml==11.5.0
220
+ - pytz==2023.3.post1
221
+ - pyyaml==6.0.1
222
+ - pyzmq==25.1.2
223
+ - referencing==0.32.0
224
+ - regex==2023.12.25
225
+ - rich==13.7.0
226
+ - rpds-py==0.16.2
227
+ - safetensors==0.4.2
228
+ - scikit-learn==1.3.2
229
+ - sentencepiece==0.1.99
230
+ - shtab==1.6.5
231
+ - sniffio==1.3.1
232
+ - soupsieve==2.5
233
+ - sympy==1.12
234
+ - termcolor==2.4.0
235
+ - threadpoolctl==3.2.0
236
+ - tinycss2==1.2.1
237
+ - tokenizers==0.15.1
238
+ - torch==2.2.1
239
+ - tornado==6.4
240
+ - tqdm==4.66.1
241
+ - traitlets==5.14.0
242
+ - transformers==4.38.1
243
+ - triton==2.2.0
244
+ - trl==0.7.11
245
+ - typing-extensions==4.9.0
246
+ - tyro==0.7.2
247
+ - tzdata==2023.3
248
+ - urllib3==2.1.0
249
+ - webencodings==0.5.1
250
+ - xxhash==3.4.1
251
+ - yarl==1.9.4
252
+ prefix: /home/bingyuew/miniconda3/envs/aspdac24
example.py ADDED
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1
+ ##########################
2
+ #Must include these lines#
3
+ ##########################
4
+ import os
5
+ os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
6
+ os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3"
7
+ import torch
8
+ from transformers import (
9
+ AutoModelForCausalLM,
10
+ AutoTokenizer,
11
+ pipeline,
12
+ )
13
+ from peft import PeftModel
14
+
15
+ ####################################################
16
+ #This is how you can set up Llama model on your end#
17
+ ####################################################
18
+ model = AutoModelForCausalLM.from_pretrained(
19
+ 'codellama/CodeLlama-7b-Instruct-hf',
20
+ device_map = "balanced_low_0"
21
+ )
22
+
23
+ tokenizer = AutoTokenizer.from_pretrained(
24
+ 'llama_prompt_model/tokenizer',
25
+ padding=True,
26
+ truncation=True,
27
+ device_map = "balanced_low_0"
28
+ )
29
+
30
+ model.resize_token_embeddings(len(tokenizer))
31
+
32
+ model = PeftModel.from_pretrained(model, "llama_prompt_model/model")
33
+
34
+ model = model.merge_and_unload()
35
+
36
+ ################################################
37
+ #This is how you can communicate with the model#
38
+ ################################################
39
+ pipe = pipeline(task="text-generation", model = model, tokenizer = tokenizer, max_length = 2000, pad_token_id = 2)
40
+
41
+ ##################################################
42
+ #feel free to change this line and see the result#
43
+ ##################################################
44
+ prompt = "What's your name?"
45
+
46
+ result = pipe("<s>[INST]%s[/INST]"%(prompt))[0]['generated_text']
47
+
48
+ print(result)
49
+
50
+
51
+
52
+
llama_prompt_model/model/README.md ADDED
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1
+ ---
2
+ library_name: peft
3
+ base_model: codellama/CodeLlama-7b-Instruct-hf
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
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+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ ## Technical Specifications [optional]
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+ ## Model Card Contact
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+ ### Framework versions
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+
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+ - PEFT 0.8.2
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+ "▁<EOT>"
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+ "content": "▁<EOT>",
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+ "▁<EOT>"
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+ "eot_token": "▁<EOT>",
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