add model files
Browse files- README.md +50 -90
- config.json +31 -0
- merges.txt +0 -0
- pytorch_model-00001-of-00005.bin +3 -0
- pytorch_model-00002-of-00005.bin +3 -0
- pytorch_model-00003-of-00005.bin +3 -0
- pytorch_model-00004-of-00005.bin +3 -0
- pytorch_model-00005-of-00005.bin +3 -0
- pytorch_model.bin.index.json +628 -0
- special_tokens_map.json +5 -0
- tokenizer.json +0 -0
- tokenizer_config.json +10 -0
- vocab.json +0 -0
README.md
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---
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language:
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- en
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library_name: nemo
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datasets:
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tags:
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- text generation
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- pytorch
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- causal-lm
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---
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<style>
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img {
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}
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</style>
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## Model Description
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Palmyra was primarily pretrained with English text, there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra is a member of the same family of models that only contain a decoder. As a result, it was pretrained utilizing the objective of self-supervised causal language modeling.
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Palmyra uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation in accordance with GPT-3. Read the official paper if you want more information about this.
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### Step 1: Install NeMo and dependencies
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git clone https://github.com/ericharper/apex.git
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cd apex
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git checkout nm_v1.11.0
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pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
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```
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```
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pip install nemo_toolkit['nlp']==1.11.0
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```
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git clone https://github.com/NVIDIA/NeMo.git
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cd NeMo/examples/nlp/language_modeling
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git checkout v1.11.0
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python megatron_gpt_eval.py gpt_model_file=palmyara_gpt_20b.nemo server=True tensor_model_parallel_size=4 trainer.devices=4
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```
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### Step 3: Send prompts to your model!
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```python
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import
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import
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port_num = 5555
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headers = {"Content-Type": "application/json"}
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def request_data(data):
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resp = requests.put('http://localhost:{}/generate'.format(port_num),
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data=json.dumps(data),
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headers=headers)
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sentences = resp.json()['sentences']
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return sentences
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data = {
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"sentences": ["Tell me an interesting fact about space travel."]*1,
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"tokens_to_generate": 50,
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"temperature": 1.0,
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"add_BOS": True,
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"top_k": 0,
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"top_p": 0.9,
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"greedy": False,
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"all_probs": False,
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"repetition_penalty": 1.2,
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"min_tokens_to_generate": 2,
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}
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sentences = request_data(data)
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print(sentences)
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```
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## Training Data
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| part | MassiveText (sampling) | tokens (B) | url | sampling ratio |
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|:---------------|-----------------------:|:----------:| :------------------------------------|---------------:|
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| mc4 filtered | MassiveWeb (48%) | 1331 | gs://mc4/final/web | 58% |
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| TrustedWeb | - | - | gs://mc4/final/trusted_web | - |
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| realnews | News (10%) | 21 | gs://mc4/final/news | 10% |
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| c4 | c4 (10%) | - | gs://mc4/final/c4 | - |
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| wikipedia-40B | wikipedia (2%) | 2 | gs://mc4/final/wikipedia | 5% |
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| github | github (3%) | - | gs://mc4/final/github | - |
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| books | books (27%) | 24 | gs://mc4/final/books | 27% |
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| youtube | - | - | gs://mc4/final/youtube | - |
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| ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
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| ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
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| 0.3976 | 0.5566 | 0.5007 | 0.4171 | 0.6133 | 0.5812 | 0.6356 | 0.6298 | 0.7492 |
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## Limitations
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The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
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[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
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## Licence
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---
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language:
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- en
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datasets:
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- English
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tags:
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- text generation
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- pytorch
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- causal-lm
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- Writer-data
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- gpt
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- NeMo
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pipeline_tag: text-generation
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library_name: transformers
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---
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license: cc-by-4.0
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# Palmyra Large 20B
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<style>
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img {
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}
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</style>
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|[](#model-architecture)|[](#model-architecture)|[](#datasets)
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## Model Description
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Palmyra Large was primarily pre-trained with English text. Note that there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra Large is a member of the same family of models that only contain a decoder. As a result, it was pre-trained utilizing the objective of self-supervised causal language modeling. Palmyra Large uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation per GPT-3.
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### Use case
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Palmyra Large is extremely powerful while being extremely fast. This model excels at many nuanced tasks such as sentiment classification and summarization.
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## Training data
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Palmyra Large (20b) was trained on Writer’s custom dataset.
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## Intended Use and Limitations
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Palmyra Large learns an inner representation of the English language that can be used to extract features useful for downstream tasks. However, the model is best at what it was pre-trained for which is generating text from a prompt.
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### How to use
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This model can be easily loaded using the `AutoModelForCausalLM` functionality:
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```python
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# set HF environment variable
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auth_token = os.environ.get("HF_TOKEN", True)
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model = AutoModelForCausalLM.from_pretrained(
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"Writer/palmyra-large",
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device_map="auto",
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torch_dtype=torch.float16,
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use_auth_token=auth_token,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"Writer/palmyra-large", use_auth_token=auth_token
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)
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```
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### Limitations and Biases
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Palmyra Large’s core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting Palmyra Large, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on Palmyra Large to produce factually correct results.
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Palmyra Large was trained on Writer’s custom data. As with all language models, it is difficult to predict how Palmyra Large will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
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## Citation and Related Information
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To cite this model:
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```
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@misc{Palmyra,
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author = {Writer Engineering team},
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title = {{Palmyra-Large Parameter Autoregressive Language Model}},
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howpublished = {\url{https://dev.writer.com}},
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year = 2023,
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month = March
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}
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```
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config.json
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{
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"activation_function": "gelu",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"initializer_range": 0.008165,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_embd": 6144,
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"n_head": 48,
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"n_inner": 24576,
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"n_layer": 44,
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"n_positions": 2048,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.21.2",
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"use_cache": true,
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"vocab_size": 50257
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}
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merges.txt
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pytorch_model-00001-of-00005.bin
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version https://git-lfs.github.com/spec/v1
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size 9796367749
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pytorch_model.bin.index.json
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"transformer.h.8.attn.c_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 600 |
+
"transformer.h.8.attn.masked_bias": "pytorch_model-00001-of-00005.bin",
|
| 601 |
+
"transformer.h.8.ln_1.bias": "pytorch_model-00001-of-00005.bin",
|
| 602 |
+
"transformer.h.8.ln_1.weight": "pytorch_model-00001-of-00005.bin",
|
| 603 |
+
"transformer.h.8.ln_2.bias": "pytorch_model-00001-of-00005.bin",
|
| 604 |
+
"transformer.h.8.ln_2.weight": "pytorch_model-00001-of-00005.bin",
|
| 605 |
+
"transformer.h.8.mlp.c_fc.bias": "pytorch_model-00001-of-00005.bin",
|
| 606 |
+
"transformer.h.8.mlp.c_fc.weight": "pytorch_model-00001-of-00005.bin",
|
| 607 |
+
"transformer.h.8.mlp.c_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 608 |
+
"transformer.h.8.mlp.c_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 609 |
+
"transformer.h.9.attn.bias": "pytorch_model-00001-of-00005.bin",
|
| 610 |
+
"transformer.h.9.attn.c_attn.bias": "pytorch_model-00001-of-00005.bin",
|
| 611 |
+
"transformer.h.9.attn.c_attn.weight": "pytorch_model-00001-of-00005.bin",
|
| 612 |
+
"transformer.h.9.attn.c_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 613 |
+
"transformer.h.9.attn.c_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 614 |
+
"transformer.h.9.attn.masked_bias": "pytorch_model-00001-of-00005.bin",
|
| 615 |
+
"transformer.h.9.ln_1.bias": "pytorch_model-00001-of-00005.bin",
|
| 616 |
+
"transformer.h.9.ln_1.weight": "pytorch_model-00001-of-00005.bin",
|
| 617 |
+
"transformer.h.9.ln_2.bias": "pytorch_model-00001-of-00005.bin",
|
| 618 |
+
"transformer.h.9.ln_2.weight": "pytorch_model-00001-of-00005.bin",
|
| 619 |
+
"transformer.h.9.mlp.c_fc.bias": "pytorch_model-00001-of-00005.bin",
|
| 620 |
+
"transformer.h.9.mlp.c_fc.weight": "pytorch_model-00001-of-00005.bin",
|
| 621 |
+
"transformer.h.9.mlp.c_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 622 |
+
"transformer.h.9.mlp.c_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 623 |
+
"transformer.ln_f.bias": "pytorch_model-00005-of-00005.bin",
|
| 624 |
+
"transformer.ln_f.weight": "pytorch_model-00005-of-00005.bin",
|
| 625 |
+
"transformer.wpe.weight": "pytorch_model-00001-of-00005.bin",
|
| 626 |
+
"transformer.wte.weight": "pytorch_model-00001-of-00005.bin"
|
| 627 |
+
}
|
| 628 |
+
}
|
special_tokens_map.json
ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": "<|endoftext|>",
|
| 3 |
+
"eos_token": "<|endoftext|>",
|
| 4 |
+
"unk_token": "<|endoftext|>"
|
| 5 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"bos_token": "<|endoftext|>",
|
| 4 |
+
"eos_token": "<|endoftext|>",
|
| 5 |
+
"model_max_length": 1024,
|
| 6 |
+
"name_or_path": "gpt2",
|
| 7 |
+
"special_tokens_map_file": null,
|
| 8 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 9 |
+
"unk_token": "<|endoftext|>"
|
| 10 |
+
}
|
vocab.json
ADDED
|
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|
|
|