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+ LLAMA 2 COMMUNITY LICENSE AGREEMENT
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+ Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
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+ ---
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+ language:
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+ - en
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+ license: llama2
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+ datasets:
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+ - OpenAssistant/oasst1
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+ - shahules786/orca-best
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+ model_name: CodeLlama 13B SFT v10
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+ base_model: OpenAssistant/codellama-13b-oasst-sft-v10
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+ inference: false
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+ model_creator: OpenAssistant
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+ model_type: llama
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+ prompt_template: '<|im_start|>system
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+
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+ {system_message}<|im_end|>
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+
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+ <|im_start|>user
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+
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+ {prompt}<|im_end|>
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+
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+ <|im_start|>assistant
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+
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+ '
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+ quantized_by: TheBloke
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+ ---
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+
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+ <!-- header start -->
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+ <!-- 200823 -->
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+ <div style="width: auto; margin-left: auto; margin-right: auto">
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+ <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+ </div>
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+ <div style="display: flex; justify-content: space-between; width: 100%;">
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+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
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+ <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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+ </div>
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+ <div style="display: flex; flex-direction: column; align-items: flex-end;">
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+ <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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+ </div>
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+ </div>
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+ <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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+ <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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+ <!-- header end -->
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+
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+ # CodeLlama 13B SFT v10 - GPTQ
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+ - Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
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+ - Original model: [CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
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+
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+ <!-- description start -->
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+ ## Description
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+
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+ This repo contains GPTQ model files for [OpenAssistant's CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10).
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+
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+ Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
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+
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+ <!-- description end -->
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+ <!-- repositories-available start -->
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+ ## Repositories available
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+
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+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-AWQ)
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+ * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ)
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+ * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
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+ * [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
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+ <!-- repositories-available end -->
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+
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+ <!-- prompt-template start -->
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+ ## Prompt template: ChatML
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+
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+ ```
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+ <|im_start|>system
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+ {system_message}<|im_end|>
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+ <|im_start|>user
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+ {prompt}<|im_end|>
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+ <|im_start|>assistant
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+
75
+ ```
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+
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+ <!-- prompt-template end -->
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+
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+
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+ <!-- README_GPTQ.md-provided-files start -->
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+ ## Provided files and GPTQ parameters
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+
83
+ Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
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+
85
+ Each separate quant is in a different branch. See below for instructions on fetching from different branches.
86
+
87
+ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
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+
89
+ <details>
90
+ <summary>Explanation of GPTQ parameters</summary>
91
+
92
+ - Bits: The bit size of the quantised model.
93
+ - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
94
+ - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
95
+ - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
96
+ - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
97
+ - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
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+ - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
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+
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+ </details>
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+
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+ | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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+ | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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+ | [main](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. |
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+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
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+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
107
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
108
+ | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
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+ | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
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+
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+ <!-- README_GPTQ.md-provided-files end -->
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+
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+ <!-- README_GPTQ.md-download-from-branches start -->
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+ ## How to download from branches
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+
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+ - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
117
+ - With Git, you can clone a branch with:
118
+ ```
119
+ git clone --single-branch --branch main https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ
120
+ ```
121
+ - In Python Transformers code, the branch is the `revision` parameter; see below.
122
+ <!-- README_GPTQ.md-download-from-branches end -->
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+ <!-- README_GPTQ.md-text-generation-webui start -->
124
+ ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
125
+
126
+ Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
127
+
128
+ It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
129
+
130
+ 1. Click the **Model tab**.
131
+ 2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ`.
132
+ - To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
133
+ - see Provided Files above for the list of branches for each option.
134
+ 3. Click **Download**.
135
+ 4. The model will start downloading. Once it's finished it will say "Done".
136
+ 5. In the top left, click the refresh icon next to **Model**.
137
+ 6. In the **Model** dropdown, choose the model you just downloaded: `CodeLlama-13B-oasst-sft-v10-GPTQ`
138
+ 7. The model will automatically load, and is now ready for use!
139
+ 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
140
+ * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
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+ 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
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+ <!-- README_GPTQ.md-text-generation-webui end -->
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+
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+ <!-- README_GPTQ.md-use-from-python start -->
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+ ## How to use this GPTQ model from Python code
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+
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+ ### Install the necessary packages
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+
149
+ Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
150
+
151
+ ```shell
152
+ pip3 install transformers>=4.32.0 optimum>=1.12.0
153
+ pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
154
+ ```
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+
156
+ If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
157
+
158
+ ```shell
159
+ pip3 uninstall -y auto-gptq
160
+ git clone https://github.com/PanQiWei/AutoGPTQ
161
+ cd AutoGPTQ
162
+ pip3 install .
163
+ ```
164
+
165
+ ### For CodeLlama models only: you must use Transformers 4.33.0 or later.
166
+
167
+ If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
168
+ ```shell
169
+ pip3 uninstall -y transformers
170
+ pip3 install git+https://github.com/huggingface/transformers.git
171
+ ```
172
+
173
+ ### You can then use the following code
174
+
175
+ ```python
176
+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
177
+
178
+ model_name_or_path = "TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ"
179
+ # To use a different branch, change revision
180
+ # For example: revision="main"
181
+ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
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+ device_map="auto",
183
+ trust_remote_code=False,
184
+ revision="main")
185
+
186
+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
187
+
188
+ prompt = "Tell me about AI"
189
+ prompt_template=f'''<|im_start|>system
190
+ {system_message}<|im_end|>
191
+ <|im_start|>user
192
+ {prompt}<|im_end|>
193
+ <|im_start|>assistant
194
+
195
+ '''
196
+
197
+ print("\n\n*** Generate:")
198
+
199
+ input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
200
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
201
+ print(tokenizer.decode(output[0]))
202
+
203
+ # Inference can also be done using transformers' pipeline
204
+
205
+ print("*** Pipeline:")
206
+ pipe = pipeline(
207
+ "text-generation",
208
+ model=model,
209
+ tokenizer=tokenizer,
210
+ max_new_tokens=512,
211
+ do_sample=True,
212
+ temperature=0.7,
213
+ top_p=0.95,
214
+ top_k=40,
215
+ repetition_penalty=1.1
216
+ )
217
+
218
+ print(pipe(prompt_template)[0]['generated_text'])
219
+ ```
220
+ <!-- README_GPTQ.md-use-from-python end -->
221
+
222
+ <!-- README_GPTQ.md-compatibility start -->
223
+ ## Compatibility
224
+
225
+ The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
226
+
227
+ [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
228
+
229
+ [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
230
+ <!-- README_GPTQ.md-compatibility end -->
231
+
232
+ <!-- footer start -->
233
+ <!-- 200823 -->
234
+ ## Discord
235
+
236
+ For further support, and discussions on these models and AI in general, join us at:
237
+
238
+ [TheBloke AI's Discord server](https://discord.gg/theblokeai)
239
+
240
+ ## Thanks, and how to contribute
241
+
242
+ Thanks to the [chirper.ai](https://chirper.ai) team!
243
+
244
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
245
+
246
+ I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
247
+
248
+ If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
249
+
250
+ Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
251
+
252
+ * Patreon: https://patreon.com/TheBlokeAI
253
+ * Ko-Fi: https://ko-fi.com/TheBlokeAI
254
+
255
+ **Special thanks to**: Aemon Algiz.
256
+
257
+ **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
258
+
259
+
260
+ Thank you to all my generous patrons and donaters!
261
+
262
+ And thank you again to a16z for their generous grant.
263
+
264
+ <!-- footer end -->
265
+
266
+ # Original model card: OpenAssistant's CodeLlama 13B SFT v10
267
+
268
+ # Open-Assistant CodeLlama 13B SFT v10
269
+
270
+ This model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B LLM.
271
+
272
+ **Note**: Due to the new RoPE Theta value (1e6 instead of 1e4), for correct results you must load this model with `trust_remote_code=True` or use the latest main branch of Huggingface transformers (until version 4.33 is released).
273
+
274
+ ## Model Details
275
+
276
+ - **Finetuned from:** [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) via [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)
277
+ - **Model type:** Causal decoder-only transformer language model
278
+ - **Language:** English
279
+ - **Weights & Biases training logs:** 6123 steps, BS 64 [run56_oa_llamacode](https://wandb.ai/open-assistant/public-sft/runs/run56_oa_llamacode)
280
+ - **Demo:** [Continuations for 250 random prompts (without system message)](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-08-26_OpenAssistant_codellama-13b-oasst-sft-v10_sampling_noprefix2.json)
281
+ - **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
282
+ - **Contact:** [Open-Assistant Discord](https://ykilcher.com/open-assistant-discord)
283
+
284
+ ## Prompting / Prompt Template
285
+
286
+ Due to public demand (see [survey](https://twitter.com/erhartford/status/1682403597525430272)) we changed the prompt-template for this model from custom prompter/assistant tokens to OpenAI's [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) standard prompt format.
287
+ We hope that this leads to greater compatibility with chat inference/frontend applications.
288
+
289
+ Prompt dialogue template:
290
+
291
+ ```
292
+ """
293
+ <|im_start|>system
294
+ {system_message}<|im_end|>
295
+ <|im_start|>user
296
+ {prompt}<|im_end|>
297
+ <|im_start|>assistant
298
+ """
299
+ ```
300
+
301
+ The model input can contain multiple conversation turns between user and assistant, e.g.
302
+ ```
303
+ <|im_start|>user
304
+ {prompt 1}<|im_end|>
305
+ <|im_start|>assistant
306
+ {reply 1}<|im_end|>
307
+ <|im_start|>user
308
+ {prompt 2}<|im_end|>
309
+ <|im_start|>assistant
310
+ (...)
311
+ ```
312
+
313
+ The model was partly trained with orca system messages.
314
+ For inference we recommend to use the official [Llama2 system message](https://github.com/facebookresearch/llama/blob/ea9f33d6d3ea8ed7d560d270986407fd6c2e52b7/example_chat_completion.py#L57-L61):
315
+ ```
316
+ <|im_start|>system
317
+ You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
318
+
319
+ If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
320
+ <|im_end|>
321
+ ```
322
+
323
+ ### Credits & Special Thanks
324
+
325
+ - Thanks to [Meta AI](https://ai.meta.com/) for training and releasing the CodeLLlama model.
326
+ - Distributed training support was provided by EPFL's [Machine Learning and Optimization Laboratory](https://www.epfl.ch/labs/mlo/), and [Natural Language Processing Lab](https://nlp.epfl.ch/).
327
+ - The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning.
328
+ - [rombodawg](https://huggingface.co/rombodawg) curated the [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored) dataset.
329
+ - [ehartford](https://huggingface.co/ehartford) generated and published the [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin).
330
+ - [shahules786](https://github.com/shahules786) de-duped and filtered the Dolphin and Megacode dataset with a clustering/controid approach and generated orca-best & bestofmegacode.
331
+ - [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.
332
+
333
+ ## Ethical Considerations and Limitations
334
+
335
+ Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios.
336
+ For these reasons, as with all LLMs, the potential outputs of codellama-13b-oasst-sft-v10 cannot be predicted
337
+ in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
338
+ to user prompts. Therefore, before deploying any applications of codellama-13b-oasst-sft-v10, developers should
339
+ perform safety testing and tuning tailored to their specific applications of the model.
340
+
341
+ Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
342
+
343
+ ## Configuration Details
344
+
345
+ The "pretokenizer" utility used to tokenize the datamix is part of the Open-Assistant github repository and can be found here: [model/pretokenizer](https://github.com/LAION-AI/Open-Assistant/tree/main/model/pretokenizer).
346
+
347
+
348
+ ### Pretokenizer Configuration
349
+
350
+
351
+ ```
352
+ orca_megacode_oasst_best:
353
+ datasets:
354
+ - orca-chat:
355
+ val_split: 0.01
356
+ max_val_set: 1000
357
+ - bestofmegacode:
358
+ val_split: 0.01
359
+ max_val_set: 1000
360
+ - oasst_export:
361
+ lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
362
+ #hf_dataset_name: OpenAssistant/oasst1
363
+ input_file_path: 2023-08-25_oasst_ready.jsonl.gz
364
+ top_k: 1
365
+ val_split: 0.025
366
+ output_dir: "output/orca_megacode_oasst_best"
367
+ filename_prefix: "orca_megacode_oasst_best"
368
+ min_assistant_tokens: 1
369
+ ```
370
+
codeLlama/USE_POLICY.md ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Llama 2 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
4
+
5
+ ## Prohibited Uses
6
+ We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
7
+
8
+ 1. Violate the law or others’ rights, including to:
9
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
10
+ 1. Violence or terrorism
11
+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
12
+ 3. Human trafficking, exploitation, and sexual violence
13
+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
14
+ 5. Sexual solicitation
15
+ 6. Any other criminal activity
16
+ 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
17
+ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
18
+ 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
19
+ 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
20
+ 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
21
+ 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
22
+
23
+
24
+
25
+ 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
26
+ 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
27
+ 2. Guns and illegal weapons (including weapon development)
28
+ 3. Illegal drugs and regulated/controlled substances
29
+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
30
+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
31
+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
32
+
33
+
34
+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
36
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
37
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
38
+ 3. Generating, promoting, or further distributing spam
39
+ 4. Impersonating another individual without consent, authorization, or legal right
40
+ 5. Representing that the use of Llama 2 or outputs are human-generated
41
+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
42
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
43
+
44
+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
45
+
46
+ * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
47
+ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
48
+ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
49
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
50
+
codeLlama/added_tokens.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "<CLS>": 32016,
3
+ "<EOD>": 32018,
4
+ "<MASK>": 32019,
5
+ "<PAD>": 32020,
6
+ "<SEP>": 32017,
7
+ "<|im_end|>": 32022,
8
+ "<|im_start|>": 32021
9
+ }
codeLlama/config.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "OpenAssistant/codellama-13b-oasst-sft-v10",
3
+ "architectures": [
4
+ "LlamaForCausalLM"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_llama.LlamaConfig",
8
+ "AutoModel": "modeling_llama.LlamaModel",
9
+ "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM",
10
+ "AutoModelForSequenceClassification": "modeling_llama.LlamaForSequenceClassification"
11
+ },
12
+ "bos_token_id": 32021,
13
+ "eos_token_id": 32022,
14
+ "hidden_act": "silu",
15
+ "hidden_size": 5120,
16
+ "initializer_range": 0.02,
17
+ "intermediate_size": 13824,
18
+ "max_position_embeddings": 16384,
19
+ "model_type": "llama",
20
+ "num_attention_heads": 40,
21
+ "num_hidden_layers": 40,
22
+ "num_key_value_heads": 40,
23
+ "pad_token_id": 0,
24
+ "pretraining_tp": 1,
25
+ "rms_norm_eps": 1e-05,
26
+ "rope_scaling": null,
27
+ "rope_theta": 1000000,
28
+ "tie_word_embeddings": false,
29
+ "torch_dtype": "bfloat16",
30
+ "transformers_version": "4.31.0",
31
+ "use_cache": true,
32
+ "vocab_size": 32032,
33
+ "quantization_config": {
34
+ "bits": 4,
35
+ "group_size": 128,
36
+ "damp_percent": 0.1,
37
+ "desc_act": false,
38
+ "sym": true,
39
+ "true_sequential": true,
40
+ "model_name_or_path": null,
41
+ "model_file_base_name": "model",
42
+ "quant_method": "gptq"
43
+ }
44
+ }
codeLlama/configuration_llama.py ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ LLaMA model configuration"""
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
29
+
30
+
31
+ class LlamaConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
34
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the LLaMA-7B.
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+
41
+ Args:
42
+ vocab_size (`int`, *optional*, defaults to 32000):
43
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
44
+ `inputs_ids` passed when calling [`LlamaModel`]
45
+ hidden_size (`int`, *optional*, defaults to 4096):
46
+ Dimension of the hidden representations.
47
+ intermediate_size (`int`, *optional*, defaults to 11008):
48
+ Dimension of the MLP representations.
49
+ num_hidden_layers (`int`, *optional*, defaults to 32):
50
+ Number of hidden layers in the Transformer encoder.
51
+ num_attention_heads (`int`, *optional*, defaults to 32):
52
+ Number of attention heads for each attention layer in the Transformer encoder.
53
+ num_key_value_heads (`int`, *optional*):
54
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58
+ by meanpooling all the original heads within that group. For more details checkout [this
59
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60
+ `num_attention_heads`.
61
+ pretraining_tp (`int`, *optional*, defaults to `1`):
62
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
63
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
64
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
65
+ issue](https://github.com/pytorch/pytorch/issues/76232).
66
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
67
+ The non-linear activation function (function or string) in the decoder.
68
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
69
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
70
+ just in case (e.g., 512 or 1024 or 2048).
71
+ initializer_range (`float`, *optional*, defaults to 0.02):
72
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
73
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
74
+ The epsilon used by the rms normalization layers.
75
+ use_cache (`bool`, *optional*, defaults to `True`):
76
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
77
+ relevant if `config.is_decoder=True`.
78
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
79
+ Whether to tie weight embeddings
80
+ rope_scaling (`Dict`, *optional*):
81
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
82
+ strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
83
+ is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
84
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
85
+ these scaling strategies behave:
86
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
87
+ experimental feature, subject to breaking API changes in future versions.
88
+
89
+ Example:
90
+
91
+ ```python
92
+ >>> from transformers import LlamaModel, LlamaConfig
93
+
94
+ >>> # Initializing a LLaMA llama-7b style configuration
95
+ >>> configuration = LlamaConfig()
96
+
97
+ >>> # Initializing a model from the llama-7b style configuration
98
+ >>> model = LlamaModel(configuration)
99
+
100
+ >>> # Accessing the model configuration
101
+ >>> configuration = model.config
102
+ ```"""
103
+ model_type = "llama"
104
+ keys_to_ignore_at_inference = ["past_key_values"]
105
+
106
+ def __init__(
107
+ self,
108
+ vocab_size=32000,
109
+ hidden_size=4096,
110
+ intermediate_size=11008,
111
+ num_hidden_layers=32,
112
+ num_attention_heads=32,
113
+ num_key_value_heads=None,
114
+ hidden_act="silu",
115
+ max_position_embeddings=2048,
116
+ initializer_range=0.02,
117
+ rms_norm_eps=1e-6,
118
+ use_cache=True,
119
+ pad_token_id=None,
120
+ bos_token_id=1,
121
+ eos_token_id=2,
122
+ pretraining_tp=1,
123
+ tie_word_embeddings=False,
124
+ rope_scaling=None,
125
+ rope_theta=10000,
126
+ **kwargs,
127
+ ):
128
+ self.vocab_size = vocab_size
129
+ self.max_position_embeddings = max_position_embeddings
130
+ self.hidden_size = hidden_size
131
+ self.intermediate_size = intermediate_size
132
+ self.num_hidden_layers = num_hidden_layers
133
+ self.num_attention_heads = num_attention_heads
134
+
135
+ # for backward compatibility
136
+ if num_key_value_heads is None:
137
+ num_key_value_heads = num_attention_heads
138
+
139
+ self.num_key_value_heads = num_key_value_heads
140
+ self.hidden_act = hidden_act
141
+ self.initializer_range = initializer_range
142
+ self.rms_norm_eps = rms_norm_eps
143
+ self.pretraining_tp = pretraining_tp
144
+ self.use_cache = use_cache
145
+ self.rope_scaling = rope_scaling
146
+ self._rope_scaling_validation()
147
+ self.rope_theta = rope_theta
148
+
149
+ super().__init__(
150
+ pad_token_id=pad_token_id,
151
+ bos_token_id=bos_token_id,
152
+ eos_token_id=eos_token_id,
153
+ tie_word_embeddings=tie_word_embeddings,
154
+ **kwargs,
155
+ )
156
+
157
+ def _rope_scaling_validation(self):
158
+ """
159
+ Validate the `rope_scaling` configuration.
160
+ """
161
+ if self.rope_scaling is None:
162
+ return
163
+
164
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
165
+ raise ValueError(
166
+ "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
167
+ f"got {self.rope_scaling}"
168
+ )
169
+ rope_scaling_type = self.rope_scaling.get("type", None)
170
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
171
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
172
+ raise ValueError(
173
+ f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
174
+ )
175
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
176
+ raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
codeLlama/generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 32021,
4
+ "eos_token_id": 32022,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.31.0"
7
+ }
codeLlama/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b43c95b4abc6e677675763c966222a87a889bdff6d05551a43f369f76fe29587
3
+ size 7260090632
codeLlama/modeling_llama.py ADDED
@@ -0,0 +1,1020 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch LLaMA model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
34
+ from .configuration_llama import LlamaConfig
35
+
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ _CONFIG_FOR_DOC = "LlamaConfig"
40
+
41
+
42
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
43
+ def _make_causal_mask(
44
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
45
+ ):
46
+ """
47
+ Make causal mask used for bi-directional self-attention.
48
+ """
49
+ bsz, tgt_len = input_ids_shape
50
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
51
+ mask_cond = torch.arange(mask.size(-1), device=device)
52
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
53
+ mask = mask.to(dtype)
54
+
55
+ if past_key_values_length > 0:
56
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
57
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
58
+
59
+
60
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
61
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
62
+ """
63
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
64
+ """
65
+ bsz, src_len = mask.size()
66
+ tgt_len = tgt_len if tgt_len is not None else src_len
67
+
68
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
69
+
70
+ inverted_mask = 1.0 - expanded_mask
71
+
72
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
73
+
74
+
75
+ class LlamaRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ LlamaRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ input_dtype = hidden_states.dtype
86
+ hidden_states = hidden_states.to(torch.float32)
87
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
88
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
89
+ return self.weight * hidden_states.to(input_dtype)
90
+
91
+
92
+ class LlamaRotaryEmbedding(torch.nn.Module):
93
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
94
+ super().__init__()
95
+
96
+ self.dim = dim
97
+ self.max_position_embeddings = max_position_embeddings
98
+ self.base = base
99
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
100
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
101
+
102
+ # Build here to make `torch.jit.trace` work.
103
+ self._set_cos_sin_cache(
104
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
105
+ )
106
+
107
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
108
+ self.max_seq_len_cached = seq_len
109
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
110
+
111
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
112
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
113
+ emb = torch.cat((freqs, freqs), dim=-1)
114
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
115
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
116
+
117
+ def forward(self, x, seq_len=None):
118
+ # x: [bs, num_attention_heads, seq_len, head_size]
119
+ if seq_len > self.max_seq_len_cached:
120
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
121
+
122
+ return (
123
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
124
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
125
+ )
126
+
127
+
128
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
129
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
130
+
131
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
132
+ self.scaling_factor = scaling_factor
133
+ super().__init__(dim, max_position_embeddings, base, device)
134
+
135
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
136
+ self.max_seq_len_cached = seq_len
137
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
138
+ t = t / self.scaling_factor
139
+
140
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
141
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
144
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
145
+
146
+
147
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
148
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
149
+
150
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
151
+ self.scaling_factor = scaling_factor
152
+ super().__init__(dim, max_position_embeddings, base, device)
153
+
154
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
155
+ self.max_seq_len_cached = seq_len
156
+
157
+ if seq_len > self.max_position_embeddings:
158
+ base = self.base * (
159
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
160
+ ) ** (self.dim / (self.dim - 2))
161
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
162
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
163
+
164
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
165
+
166
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
167
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
168
+ emb = torch.cat((freqs, freqs), dim=-1)
169
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
170
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
171
+
172
+
173
+ def rotate_half(x):
174
+ """Rotates half the hidden dims of the input."""
175
+ x1 = x[..., : x.shape[-1] // 2]
176
+ x2 = x[..., x.shape[-1] // 2 :]
177
+ return torch.cat((-x2, x1), dim=-1)
178
+
179
+
180
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
181
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
182
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
183
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
184
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
185
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
186
+ q_embed = (q * cos) + (rotate_half(q) * sin)
187
+ k_embed = (k * cos) + (rotate_half(k) * sin)
188
+ return q_embed, k_embed
189
+
190
+
191
+ class LlamaMLP(nn.Module):
192
+ def __init__(self, config):
193
+ super().__init__()
194
+ self.config = config
195
+ self.hidden_size = config.hidden_size
196
+ self.intermediate_size = config.intermediate_size
197
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
198
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
199
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
200
+ self.act_fn = ACT2FN[config.hidden_act]
201
+
202
+ def forward(self, x):
203
+ if self.config.pretraining_tp > 1:
204
+ slice = self.intermediate_size // self.config.pretraining_tp
205
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
206
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
207
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
208
+
209
+ gate_proj = torch.cat(
210
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
211
+ )
212
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
213
+
214
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
215
+ down_proj = [
216
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
217
+ ]
218
+ down_proj = sum(down_proj)
219
+ else:
220
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
221
+
222
+ return down_proj
223
+
224
+
225
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
226
+ """
227
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
228
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
229
+ """
230
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
231
+ if n_rep == 1:
232
+ return hidden_states
233
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
234
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
235
+
236
+
237
+ class LlamaAttention(nn.Module):
238
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
239
+
240
+ def __init__(self, config: LlamaConfig):
241
+ super().__init__()
242
+ self.config = config
243
+ self.hidden_size = config.hidden_size
244
+ self.num_heads = config.num_attention_heads
245
+ self.head_dim = self.hidden_size // self.num_heads
246
+ self.num_key_value_heads = config.num_key_value_heads
247
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
248
+ self.max_position_embeddings = config.max_position_embeddings
249
+ self.rope_theta = config.rope_theta
250
+
251
+ if (self.head_dim * self.num_heads) != self.hidden_size:
252
+ raise ValueError(
253
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
254
+ f" and `num_heads`: {self.num_heads})."
255
+ )
256
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
257
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
258
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
259
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
260
+ self._init_rope()
261
+
262
+ def _init_rope(self):
263
+ if self.config.rope_scaling is None:
264
+ self.rotary_emb = LlamaRotaryEmbedding(
265
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
266
+ base=self.rope_theta
267
+ )
268
+ else:
269
+ scaling_type = self.config.rope_scaling["type"]
270
+ scaling_factor = self.config.rope_scaling["factor"]
271
+ if scaling_type == "linear":
272
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
273
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
274
+ base=self.rope_theta, scaling_factor=scaling_factor
275
+ )
276
+ elif scaling_type == "dynamic":
277
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
278
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
279
+ base=self.rope_theta, scaling_factor=scaling_factor
280
+ )
281
+ else:
282
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
283
+
284
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
285
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
286
+
287
+ def forward(
288
+ self,
289
+ hidden_states: torch.Tensor,
290
+ attention_mask: Optional[torch.Tensor] = None,
291
+ position_ids: Optional[torch.LongTensor] = None,
292
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
293
+ output_attentions: bool = False,
294
+ use_cache: bool = False,
295
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
296
+ bsz, q_len, _ = hidden_states.size()
297
+
298
+ if self.config.pretraining_tp > 1:
299
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
300
+ query_slices = self.q_proj.weight.split(
301
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
302
+ )
303
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
304
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
305
+
306
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
307
+ query_states = torch.cat(query_states, dim=-1)
308
+
309
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
310
+ key_states = torch.cat(key_states, dim=-1)
311
+
312
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
313
+ value_states = torch.cat(value_states, dim=-1)
314
+
315
+ else:
316
+ query_states = self.q_proj(hidden_states)
317
+ key_states = self.k_proj(hidden_states)
318
+ value_states = self.v_proj(hidden_states)
319
+
320
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
321
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
322
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
323
+
324
+ kv_seq_len = key_states.shape[-2]
325
+ if past_key_value is not None:
326
+ kv_seq_len += past_key_value[0].shape[-2]
327
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
328
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
329
+
330
+ if past_key_value is not None:
331
+ # reuse k, v, self_attention
332
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
333
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
334
+
335
+ past_key_value = (key_states, value_states) if use_cache else None
336
+
337
+ # repeat k/v heads if n_kv_heads < n_heads
338
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
339
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
340
+
341
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
342
+
343
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
344
+ raise ValueError(
345
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
346
+ f" {attn_weights.size()}"
347
+ )
348
+
349
+ if attention_mask is not None:
350
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
351
+ raise ValueError(
352
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
353
+ )
354
+ attn_weights = attn_weights + attention_mask
355
+
356
+ # upcast attention to fp32
357
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
358
+ attn_output = torch.matmul(attn_weights, value_states)
359
+
360
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
361
+ raise ValueError(
362
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
363
+ f" {attn_output.size()}"
364
+ )
365
+
366
+ attn_output = attn_output.transpose(1, 2).contiguous()
367
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
368
+
369
+ if self.config.pretraining_tp > 1:
370
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
371
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
372
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
373
+ else:
374
+ attn_output = self.o_proj(attn_output)
375
+
376
+ if not output_attentions:
377
+ attn_weights = None
378
+
379
+ return attn_output, attn_weights, past_key_value
380
+
381
+
382
+ class LlamaDecoderLayer(nn.Module):
383
+ def __init__(self, config: LlamaConfig):
384
+ super().__init__()
385
+ self.hidden_size = config.hidden_size
386
+ self.self_attn = LlamaAttention(config=config)
387
+ self.mlp = LlamaMLP(config)
388
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
389
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
390
+
391
+ def forward(
392
+ self,
393
+ hidden_states: torch.Tensor,
394
+ attention_mask: Optional[torch.Tensor] = None,
395
+ position_ids: Optional[torch.LongTensor] = None,
396
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
397
+ output_attentions: Optional[bool] = False,
398
+ use_cache: Optional[bool] = False,
399
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
400
+ """
401
+ Args:
402
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
403
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
404
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
405
+ output_attentions (`bool`, *optional*):
406
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
407
+ returned tensors for more detail.
408
+ use_cache (`bool`, *optional*):
409
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
410
+ (see `past_key_values`).
411
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
412
+ """
413
+
414
+ residual = hidden_states
415
+
416
+ hidden_states = self.input_layernorm(hidden_states)
417
+
418
+ # Self Attention
419
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
420
+ hidden_states=hidden_states,
421
+ attention_mask=attention_mask,
422
+ position_ids=position_ids,
423
+ past_key_value=past_key_value,
424
+ output_attentions=output_attentions,
425
+ use_cache=use_cache,
426
+ )
427
+ hidden_states = residual + hidden_states
428
+
429
+ # Fully Connected
430
+ residual = hidden_states
431
+ hidden_states = self.post_attention_layernorm(hidden_states)
432
+ hidden_states = self.mlp(hidden_states)
433
+ hidden_states = residual + hidden_states
434
+
435
+ outputs = (hidden_states,)
436
+
437
+ if output_attentions:
438
+ outputs += (self_attn_weights,)
439
+
440
+ if use_cache:
441
+ outputs += (present_key_value,)
442
+
443
+ return outputs
444
+
445
+
446
+ LLAMA_START_DOCSTRING = r"""
447
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
448
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
449
+ etc.)
450
+
451
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
452
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
453
+ and behavior.
454
+
455
+ Parameters:
456
+ config ([`LlamaConfig`]):
457
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
458
+ load the weights associated with the model, only the configuration. Check out the
459
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
460
+ """
461
+
462
+
463
+ @add_start_docstrings(
464
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
465
+ LLAMA_START_DOCSTRING,
466
+ )
467
+ class LlamaPreTrainedModel(PreTrainedModel):
468
+ config_class = LlamaConfig
469
+ base_model_prefix = "model"
470
+ supports_gradient_checkpointing = True
471
+ _no_split_modules = ["LlamaDecoderLayer"]
472
+ _skip_keys_device_placement = "past_key_values"
473
+
474
+ def _init_weights(self, module):
475
+ std = self.config.initializer_range
476
+ if isinstance(module, nn.Linear):
477
+ module.weight.data.normal_(mean=0.0, std=std)
478
+ if module.bias is not None:
479
+ module.bias.data.zero_()
480
+ elif isinstance(module, nn.Embedding):
481
+ module.weight.data.normal_(mean=0.0, std=std)
482
+ if module.padding_idx is not None:
483
+ module.weight.data[module.padding_idx].zero_()
484
+
485
+ def _set_gradient_checkpointing(self, module, value=False):
486
+ if isinstance(module, LlamaModel):
487
+ module.gradient_checkpointing = value
488
+
489
+
490
+ LLAMA_INPUTS_DOCSTRING = r"""
491
+ Args:
492
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
493
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
494
+ it.
495
+
496
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
497
+ [`PreTrainedTokenizer.__call__`] for details.
498
+
499
+ [What are input IDs?](../glossary#input-ids)
500
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
501
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
502
+
503
+ - 1 for tokens that are **not masked**,
504
+ - 0 for tokens that are **masked**.
505
+
506
+ [What are attention masks?](../glossary#attention-mask)
507
+
508
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
509
+ [`PreTrainedTokenizer.__call__`] for details.
510
+
511
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
512
+ `past_key_values`).
513
+
514
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
515
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
516
+ information on the default strategy.
517
+
518
+ - 1 indicates the head is **not masked**,
519
+ - 0 indicates the head is **masked**.
520
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
521
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
522
+ config.n_positions - 1]`.
523
+
524
+ [What are position IDs?](../glossary#position-ids)
525
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
526
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
527
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
528
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
529
+
530
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
531
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
532
+
533
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
534
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
535
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
536
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
537
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
538
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
539
+ model's internal embedding lookup matrix.
540
+ use_cache (`bool`, *optional*):
541
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
542
+ `past_key_values`).
543
+ output_attentions (`bool`, *optional*):
544
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
545
+ tensors for more detail.
546
+ output_hidden_states (`bool`, *optional*):
547
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
548
+ more detail.
549
+ return_dict (`bool`, *optional*):
550
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
551
+ """
552
+
553
+
554
+ @add_start_docstrings(
555
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
556
+ LLAMA_START_DOCSTRING,
557
+ )
558
+ class LlamaModel(LlamaPreTrainedModel):
559
+ """
560
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
561
+
562
+ Args:
563
+ config: LlamaConfig
564
+ """
565
+
566
+ def __init__(self, config: LlamaConfig):
567
+ super().__init__(config)
568
+ self.padding_idx = config.pad_token_id
569
+ self.vocab_size = config.vocab_size
570
+
571
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
572
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
573
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
574
+
575
+ self.gradient_checkpointing = False
576
+ # Initialize weights and apply final processing
577
+ self.post_init()
578
+
579
+ def get_input_embeddings(self):
580
+ return self.embed_tokens
581
+
582
+ def set_input_embeddings(self, value):
583
+ self.embed_tokens = value
584
+
585
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
586
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
587
+ # create causal mask
588
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
589
+ combined_attention_mask = None
590
+ if input_shape[-1] > 1:
591
+ combined_attention_mask = _make_causal_mask(
592
+ input_shape,
593
+ inputs_embeds.dtype,
594
+ device=inputs_embeds.device,
595
+ past_key_values_length=past_key_values_length,
596
+ )
597
+
598
+ if attention_mask is not None:
599
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
600
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
601
+ inputs_embeds.device
602
+ )
603
+ combined_attention_mask = (
604
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
605
+ )
606
+
607
+ return combined_attention_mask
608
+
609
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
610
+ def forward(
611
+ self,
612
+ input_ids: torch.LongTensor = None,
613
+ attention_mask: Optional[torch.Tensor] = None,
614
+ position_ids: Optional[torch.LongTensor] = None,
615
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
616
+ inputs_embeds: Optional[torch.FloatTensor] = None,
617
+ use_cache: Optional[bool] = None,
618
+ output_attentions: Optional[bool] = None,
619
+ output_hidden_states: Optional[bool] = None,
620
+ return_dict: Optional[bool] = None,
621
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
622
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
623
+ output_hidden_states = (
624
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
625
+ )
626
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
627
+
628
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
629
+
630
+ # retrieve input_ids and inputs_embeds
631
+ if input_ids is not None and inputs_embeds is not None:
632
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
633
+ elif input_ids is not None:
634
+ batch_size, seq_length = input_ids.shape
635
+ elif inputs_embeds is not None:
636
+ batch_size, seq_length, _ = inputs_embeds.shape
637
+ else:
638
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
639
+
640
+ seq_length_with_past = seq_length
641
+ past_key_values_length = 0
642
+
643
+ if past_key_values is not None:
644
+ past_key_values_length = past_key_values[0][0].shape[2]
645
+ seq_length_with_past = seq_length_with_past + past_key_values_length
646
+
647
+ if position_ids is None:
648
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
649
+ position_ids = torch.arange(
650
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
651
+ )
652
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
653
+ else:
654
+ position_ids = position_ids.view(-1, seq_length).long()
655
+
656
+ if inputs_embeds is None:
657
+ inputs_embeds = self.embed_tokens(input_ids)
658
+ # embed positions
659
+ if attention_mask is None:
660
+ attention_mask = torch.ones(
661
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
662
+ )
663
+ attention_mask = self._prepare_decoder_attention_mask(
664
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
665
+ )
666
+
667
+ hidden_states = inputs_embeds
668
+
669
+ if self.gradient_checkpointing and self.training:
670
+ if use_cache:
671
+ logger.warning_once(
672
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
673
+ )
674
+ use_cache = False
675
+
676
+ # decoder layers
677
+ all_hidden_states = () if output_hidden_states else None
678
+ all_self_attns = () if output_attentions else None
679
+ next_decoder_cache = () if use_cache else None
680
+
681
+ for idx, decoder_layer in enumerate(self.layers):
682
+ if output_hidden_states:
683
+ all_hidden_states += (hidden_states,)
684
+
685
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
686
+
687
+ if self.gradient_checkpointing and self.training:
688
+
689
+ def create_custom_forward(module):
690
+ def custom_forward(*inputs):
691
+ # None for past_key_value
692
+ return module(*inputs, past_key_value, output_attentions)
693
+
694
+ return custom_forward
695
+
696
+ layer_outputs = torch.utils.checkpoint.checkpoint(
697
+ create_custom_forward(decoder_layer),
698
+ hidden_states,
699
+ attention_mask,
700
+ position_ids,
701
+ )
702
+ else:
703
+ layer_outputs = decoder_layer(
704
+ hidden_states,
705
+ attention_mask=attention_mask,
706
+ position_ids=position_ids,
707
+ past_key_value=past_key_value,
708
+ output_attentions=output_attentions,
709
+ use_cache=use_cache,
710
+ )
711
+
712
+ hidden_states = layer_outputs[0]
713
+
714
+ if use_cache:
715
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
716
+
717
+ if output_attentions:
718
+ all_self_attns += (layer_outputs[1],)
719
+
720
+ hidden_states = self.norm(hidden_states)
721
+
722
+ # add hidden states from the last decoder layer
723
+ if output_hidden_states:
724
+ all_hidden_states += (hidden_states,)
725
+
726
+ next_cache = next_decoder_cache if use_cache else None
727
+ if not return_dict:
728
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
729
+ return BaseModelOutputWithPast(
730
+ last_hidden_state=hidden_states,
731
+ past_key_values=next_cache,
732
+ hidden_states=all_hidden_states,
733
+ attentions=all_self_attns,
734
+ )
735
+
736
+
737
+ class LlamaForCausalLM(LlamaPreTrainedModel):
738
+ _tied_weights_keys = ["lm_head.weight"]
739
+
740
+ def __init__(self, config):
741
+ super().__init__(config)
742
+ self.model = LlamaModel(config)
743
+ self.vocab_size = config.vocab_size
744
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
745
+
746
+ # Initialize weights and apply final processing
747
+ self.post_init()
748
+
749
+ def get_input_embeddings(self):
750
+ return self.model.embed_tokens
751
+
752
+ def set_input_embeddings(self, value):
753
+ self.model.embed_tokens = value
754
+
755
+ def get_output_embeddings(self):
756
+ return self.lm_head
757
+
758
+ def set_output_embeddings(self, new_embeddings):
759
+ self.lm_head = new_embeddings
760
+
761
+ def set_decoder(self, decoder):
762
+ self.model = decoder
763
+
764
+ def get_decoder(self):
765
+ return self.model
766
+
767
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
768
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
769
+ def forward(
770
+ self,
771
+ input_ids: torch.LongTensor = None,
772
+ attention_mask: Optional[torch.Tensor] = None,
773
+ position_ids: Optional[torch.LongTensor] = None,
774
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
775
+ inputs_embeds: Optional[torch.FloatTensor] = None,
776
+ labels: Optional[torch.LongTensor] = None,
777
+ use_cache: Optional[bool] = None,
778
+ output_attentions: Optional[bool] = None,
779
+ output_hidden_states: Optional[bool] = None,
780
+ return_dict: Optional[bool] = None,
781
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
782
+ r"""
783
+ Args:
784
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
785
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
786
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
787
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
788
+
789
+ Returns:
790
+
791
+ Example:
792
+
793
+ ```python
794
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
795
+
796
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
797
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
798
+
799
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
800
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
801
+
802
+ >>> # Generate
803
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
804
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
805
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
806
+ ```"""
807
+
808
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
809
+ output_hidden_states = (
810
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
811
+ )
812
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
813
+
814
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
815
+ outputs = self.model(
816
+ input_ids=input_ids,
817
+ attention_mask=attention_mask,
818
+ position_ids=position_ids,
819
+ past_key_values=past_key_values,
820
+ inputs_embeds=inputs_embeds,
821
+ use_cache=use_cache,
822
+ output_attentions=output_attentions,
823
+ output_hidden_states=output_hidden_states,
824
+ return_dict=return_dict,
825
+ )
826
+
827
+ hidden_states = outputs[0]
828
+ if self.config.pretraining_tp > 1:
829
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
830
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
831
+ logits = torch.cat(logits, dim=-1)
832
+ else:
833
+ logits = self.lm_head(hidden_states)
834
+ logits = logits.float()
835
+
836
+ loss = None
837
+ if labels is not None:
838
+ # Shift so that tokens < n predict n
839
+ shift_logits = logits[..., :-1, :].contiguous()
840
+ shift_labels = labels[..., 1:].contiguous()
841
+ # Flatten the tokens
842
+ loss_fct = CrossEntropyLoss()
843
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
844
+ shift_labels = shift_labels.view(-1)
845
+ # Enable model parallelism
846
+ shift_labels = shift_labels.to(shift_logits.device)
847
+ loss = loss_fct(shift_logits, shift_labels)
848
+
849
+ if not return_dict:
850
+ output = (logits,) + outputs[1:]
851
+ return (loss,) + output if loss is not None else output
852
+
853
+ return CausalLMOutputWithPast(
854
+ loss=loss,
855
+ logits=logits,
856
+ past_key_values=outputs.past_key_values,
857
+ hidden_states=outputs.hidden_states,
858
+ attentions=outputs.attentions,
859
+ )
860
+
861
+ def prepare_inputs_for_generation(
862
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
863
+ ):
864
+ if past_key_values:
865
+ input_ids = input_ids[:, -1:]
866
+
867
+ position_ids = kwargs.get("position_ids", None)
868
+ if attention_mask is not None and position_ids is None:
869
+ # create position_ids on the fly for batch generation
870
+ position_ids = attention_mask.long().cumsum(-1) - 1
871
+ position_ids.masked_fill_(attention_mask == 0, 1)
872
+ if past_key_values:
873
+ position_ids = position_ids[:, -1].unsqueeze(-1)
874
+
875
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
876
+ if inputs_embeds is not None and past_key_values is None:
877
+ model_inputs = {"inputs_embeds": inputs_embeds}
878
+ else:
879
+ model_inputs = {"input_ids": input_ids}
880
+
881
+ model_inputs.update(
882
+ {
883
+ "position_ids": position_ids,
884
+ "past_key_values": past_key_values,
885
+ "use_cache": kwargs.get("use_cache"),
886
+ "attention_mask": attention_mask,
887
+ }
888
+ )
889
+ return model_inputs
890
+
891
+ @staticmethod
892
+ def _reorder_cache(past_key_values, beam_idx):
893
+ reordered_past = ()
894
+ for layer_past in past_key_values:
895
+ reordered_past += (
896
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
897
+ )
898
+ return reordered_past
899
+
900
+
901
+ @add_start_docstrings(
902
+ """
903
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
904
+
905
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
906
+ (e.g. GPT-2) do.
907
+
908
+ Since it does classification on the last token, it requires to know the position of the last token. If a
909
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
910
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
911
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
912
+ each row of the batch).
913
+ """,
914
+ LLAMA_START_DOCSTRING,
915
+ )
916
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
917
+ def __init__(self, config):
918
+ super().__init__(config)
919
+ self.num_labels = config.num_labels
920
+ self.model = LlamaModel(config)
921
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
922
+
923
+ # Initialize weights and apply final processing
924
+ self.post_init()
925
+
926
+ def get_input_embeddings(self):
927
+ return self.model.embed_tokens
928
+
929
+ def set_input_embeddings(self, value):
930
+ self.model.embed_tokens = value
931
+
932
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
933
+ def forward(
934
+ self,
935
+ input_ids: torch.LongTensor = None,
936
+ attention_mask: Optional[torch.Tensor] = None,
937
+ position_ids: Optional[torch.LongTensor] = None,
938
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
939
+ inputs_embeds: Optional[torch.FloatTensor] = None,
940
+ labels: Optional[torch.LongTensor] = None,
941
+ use_cache: Optional[bool] = None,
942
+ output_attentions: Optional[bool] = None,
943
+ output_hidden_states: Optional[bool] = None,
944
+ return_dict: Optional[bool] = None,
945
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
946
+ r"""
947
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
948
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
949
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
950
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
951
+ """
952
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
953
+
954
+ transformer_outputs = self.model(
955
+ input_ids,
956
+ attention_mask=attention_mask,
957
+ position_ids=position_ids,
958
+ past_key_values=past_key_values,
959
+ inputs_embeds=inputs_embeds,
960
+ use_cache=use_cache,
961
+ output_attentions=output_attentions,
962
+ output_hidden_states=output_hidden_states,
963
+ return_dict=return_dict,
964
+ )
965
+ hidden_states = transformer_outputs[0]
966
+ logits = self.score(hidden_states)
967
+
968
+ if input_ids is not None:
969
+ batch_size = input_ids.shape[0]
970
+ else:
971
+ batch_size = inputs_embeds.shape[0]
972
+
973
+ if self.config.pad_token_id is None and batch_size != 1:
974
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
975
+ if self.config.pad_token_id is None:
976
+ sequence_lengths = -1
977
+ else:
978
+ if input_ids is not None:
979
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
980
+ logits.device
981
+ )
982
+ else:
983
+ sequence_lengths = -1
984
+
985
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
986
+
987
+ loss = None
988
+ if labels is not None:
989
+ labels = labels.to(logits.device)
990
+ if self.config.problem_type is None:
991
+ if self.num_labels == 1:
992
+ self.config.problem_type = "regression"
993
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
994
+ self.config.problem_type = "single_label_classification"
995
+ else:
996
+ self.config.problem_type = "multi_label_classification"
997
+
998
+ if self.config.problem_type == "regression":
999
+ loss_fct = MSELoss()
1000
+ if self.num_labels == 1:
1001
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1002
+ else:
1003
+ loss = loss_fct(pooled_logits, labels)
1004
+ elif self.config.problem_type == "single_label_classification":
1005
+ loss_fct = CrossEntropyLoss()
1006
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1007
+ elif self.config.problem_type == "multi_label_classification":
1008
+ loss_fct = BCEWithLogitsLoss()
1009
+ loss = loss_fct(pooled_logits, labels)
1010
+ if not return_dict:
1011
+ output = (pooled_logits,) + transformer_outputs[1:]
1012
+ return ((loss,) + output) if loss is not None else output
1013
+
1014
+ return SequenceClassifierOutputWithPast(
1015
+ loss=loss,
1016
+ logits=pooled_logits,
1017
+ past_key_values=transformer_outputs.past_key_values,
1018
+ hidden_states=transformer_outputs.hidden_states,
1019
+ attentions=transformer_outputs.attentions,
1020
+ )
codeLlama/quantize_config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bits": 4,
3
+ "group_size": 128,
4
+ "damp_percent": 0.1,
5
+ "desc_act": false,
6
+ "sym": true,
7
+ "true_sequential": true,
8
+ "model_name_or_path": null,
9
+ "model_file_base_name": "model"
10
+ }
codeLlama/special_tokens_map.json ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "bos_token": "<|im_start|>",
7
+ "cls_token": "<CLS>",
8
+ "eos_token": "<|im_end|>",
9
+ "mask_token": "<MASK>",
10
+ "pad_token": "<PAD>",
11
+ "sep_token": "<SEP>",
12
+ "unk_token": {
13
+ "content": "<unk>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false
18
+ }
19
+ }
codeLlama/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
codeLlama/tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
3
+ size 500058
codeLlama/tokenizer_config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "__type": "AddedToken",
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": true,
7
+ "rstrip": false,
8
+ "single_word": false
9
+ },
10
+ "clean_up_tokenization_spaces": false,
11
+ "eos_token": {
12
+ "__type": "AddedToken",
13
+ "content": "</s>",
14
+ "lstrip": false,
15
+ "normalized": true,
16
+ "rstrip": false,
17
+ "single_word": false
18
+ },
19
+ "legacy": true,
20
+ "model_max_length": 1000000000000000019884624838656,
21
+ "pad_token": null,
22
+ "add_bos_token": false,
23
+ "sp_model_kwargs": {},
24
+ "tokenizer_class": "LlamaTokenizer",
25
+ "unk_token": {
26
+ "__type": "AddedToken",
27
+ "content": "<unk>",
28
+ "lstrip": false,
29
+ "normalized": true,
30
+ "rstrip": false,
31
+ "single_word": false
32
+ }
33
+ }