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---
license: other
commercial: false
datasets:
- aisquared/databricks-dolly-15k
language:
- en
library_name: transformers
---
# Model Card for `chopt-1_3b`
<!-- Provide a quick summary of what the model is/does. -->
AI Squared's `chopt-1_3b` is a large language model which is derived from Meta AI's Open Pre-trained Transformer language modelsand fine-tuned on a corpus of 15k records ([Databricks' "Dolly 15k" Dataset](https://huggingface.co/datasets/aisquared/databricks-dolly-15k)) to help it exhibit chat-based capabilities. Despite the permissive license of the Dolly 15k dataset, due to this model being a derivative of OPT it is restricted to use for **non-commercial research purposes**. The ChOPT family of models from AI Squared are licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
While `chopt-1_3b` is **not a state-of-the-art model**, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** AI Squared, Inc.
- **Shared by:** AI Squared, Inc.
- **Model type:** Large Language Model
- **Language(s) (NLP):** EN
- **License:** other
- **Finetuned from model:** OPT
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
**`chopt-1_3b` is not a state-of-the-art language model.** `chopt-1_3b` is an experimental technology and is not designed for use in any
environment other than for research purposes. Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include,
but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations.
Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology.
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.
From your terminal, run:
```python
pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
```
The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline`
found in the model repo [here](https://huggingface.co/aisquared/chopt-1_3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality.
It is also fine to remove it if there is sufficient memory.
```python
from transformers import pipeline
import torch
generate_text = pipeline(model="aisquared/chopt-1_3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```
You can then use the pipeline to answer instructions:
```python
res = generate_text("Who was George Washington?")
print(res)
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/aisquared/chopt-1_3b/blob/main/instruct_pipeline.py),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("aisquared/chopt-1_3b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("aisquared/chopt-1_3b", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
```
### Model Performance Metrics
We present the results from various model benchmarks on the EleutherAI LLM Evaluation Harness for all models in the ChOPT family.
Model results are sorted by mean score, ascending, to provide an ordering. These metrics serve to further show that none of the DLite models are
state of the art, but rather further show that chat-like behaviors in LLMs can be trained almost independent of model size.
| Model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq |
|:--------------------|-------------:|-----------:|-------------:|------------:|----------------:|---------:|---------:|
| chopt-125m | 0.178 | 0.443182 | 0.501973 | 0.294165 | 0.197099 | 0.630577 | 0.476758 |
| chopt-research-125m | 0.17 | 0.436027 | 0.503552 | 0.294762 | 0.205631 | 0.62568 | 0.48685 |
| opt-125m | 0.166 | 0.435606 | 0.501973 | 0.291775 | 0.190273 | 0.6284 | 0.554434 |
| chopt-350m | 0.178 | 0.450758 | 0.508287 | 0.325334 | 0.21843 | 0.650707 | 0.559633 |
| opt_350m | 0.176 | 0.441077 | 0.52644 | 0.320056 | 0.207338 | 0.645267 | 0.57737 |
| chopt-research-350m | 0.172 | 0.462542 | 0.514601 | 0.327524 | 0.235495 | 0.643634 | 0.589908 |
| opt-1.3b | 0.234 | 0.569865 | 0.596685 | 0.414957 | 0.232935 | 0.718172 | 0.577676 |
| chopt-research-1_3b | 0.232 | 0.564815 | 0.59116 | 0.424716 | 0.276451 | 0.713275 | 0.634557 |
| chopt-1_3b | 0.236 | 0.569444 | 0.584057 | 0.42621 | 0.268771 | 0.723069 | 0.658104 |
| opt-2.7b | 0.25 | 0.608165 | 0.608524 | 0.458176 | 0.267918 | 0.738303 | 0.603058 |
| chopt-2_7b | 0.276 | 0.616582 | 0.601421 | 0.472615 | 0.288396 | 0.75136 | 0.552294 |
| chopt-research-2_7b | 0.262 | 0.610269 | 0.625099 | 0.458176 | 0.295222 | 0.742111 | 0.636697 |
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__chopt-1_3b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 30.94 |
| ARC (25-shot) | 31.48 |
| HellaSwag (10-shot) | 56.63 |
| MMLU (5-shot) | 25.35 |
| TruthfulQA (0-shot) | 40.19 |
| Winogrande (5-shot) | 58.25 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 4.67 |

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{
"### End": 50265,
"### Instruction:": 50266,
"### Response:\n": 50267
}

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{
"_name_or_path": "facebook/opt-1.3b",
"_remove_final_layer_norm": false,
"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"OPTForCausalLM"
],
"custom_pipelines": {
"text-generation": {
"impl": "instruct_pipeline.InstructionTextGenerationPipeline",
"pt": "AutoModelForCausalLM",
"tf": "TFAutoModelForCausalLM"
}
},
"attention_dropout": 0.0,
"bos_token_id": 2,
"do_layer_norm_before": true,
"dropout": 0.1,
"enable_bias": true,
"eos_token_id": 2,
"ffn_dim": 8192,
"hidden_size": 2048,
"init_std": 0.02,
"layer_norm_elementwise_affine": true,
"layerdrop": 0.0,
"max_position_embeddings": 2048,
"model_type": "opt",
"num_attention_heads": 32,
"num_hidden_layers": 24,
"pad_token_id": 1,
"prefix": "</s>",
"torch_dtype": "float16",
"transformers_version": "4.25.1",
"use_cache": false,
"vocab_size": 50268,
"word_embed_proj_dim": 2048
}

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import re
import numpy as np
from transformers import Pipeline, PreTrainedTokenizer
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
END_KEY = "### End"
INTRO_BLURB = (
"Below is an instruction that describes a task, along with any additional context. Write a response that appropriately completes the request."
)
# This is the prompt that is used for generating responses using an already trained model. It ends with the response
# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
"""Gets the token ID for a given string that has been added to the tokenizer as a special token.
When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.
Args:
tokenizer (PreTrainedTokenizer): the tokenizer
key (str): the key to convert to a single token
Raises:
RuntimeError: if more than one ID was generated
Returns:
int: the token ID for the given key
"""
token_ids = tokenizer.encode(key)
if len(token_ids) > 1:
raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
return token_ids[0]
class InstructionTextGenerationPipeline(Pipeline):
def __init__(
self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
):
super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)
def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
preprocess_params = {}
# newer versions of the tokenizer configure the response key as a special token. newer versions still may
# append a newline to yield a single token. find whatever token is configured for the response key.
tokenizer_response_key = next(
(token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
)
response_key_token_id = None
end_key_token_id = None
if tokenizer_response_key:
try:
response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
# Ensure generation stops once it generates "### End"
generate_kwargs["eos_token_id"] = end_key_token_id
except ValueError:
pass
forward_params = generate_kwargs
postprocess_params = {
"response_key_token_id": response_key_token_id,
"end_key_token_id": end_key_token_id,
"return_instruction_text": return_instruction_text,
}
return preprocess_params, forward_params, postprocess_params
def preprocess(self, instruction_text, **generate_kwargs):
prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
inputs = self.tokenizer(
prompt_text,
return_tensors="pt",
)
inputs["prompt_text"] = prompt_text
inputs["instruction_text"] = instruction_text
return inputs
def _forward(self, model_inputs, **generate_kwargs):
input_ids = model_inputs["input_ids"]
attention_mask = model_inputs.get("attention_mask", None)
generated_sequence = self.model.generate(
input_ids=input_ids.to(self.model.device),
attention_mask=attention_mask,
pad_token_id=self.tokenizer.pad_token_id,
**generate_kwargs,
)[0].cpu()
instruction_text = model_inputs.pop("instruction_text")
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text):
sequence = model_outputs["generated_sequence"]
instruction_text = model_outputs["instruction_text"]
# The response will be set to this variable if we can identify it.
decoded = None
# If we have token IDs for the response and end, then we can find the tokens and only decode between them.
if response_key_token_id and end_key_token_id:
# Find where "### Response:" is first found in the generated tokens. Considering this is part of the
# prompt, we should definitely find it. We will return the tokens found after this token.
response_pos = None
response_positions = np.where(sequence == response_key_token_id)[0]
if len(response_positions) == 0:
pass
else:
response_pos = response_positions[0]
if response_pos:
# Next find where "### End" is located. The model has been trained to end its responses with this
# sequence (or actually, the token ID it maps to, since it is a special token). We may not find
# this token, as the response could be truncated. If we don't find it then just return everything
# to the end. Note that even though we set eos_token_id, we still see the this token at the end.
end_pos = None
end_positions = np.where(sequence == end_key_token_id)[0]
if len(end_positions) > 0:
end_pos = end_positions[0]
decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
else:
# Otherwise we'll decode everything and use a regex to find the response and end.
fully_decoded = self.tokenizer.decode(sequence)
# The response appears after "### Response:". The model has been trained to append "### End" at the
# end.
m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
if m:
decoded = m.group(1).strip()
else:
# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
# return everything after "### Response:".
m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
if m:
decoded = m.group(1).strip()
if return_instruction_text:
return {"instruction_text": instruction_text, "generated_text": decoded}
return decoded

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{
"additional_special_tokens": [
"### End",
"### Instruction:",
"### Response:\n"
],
"bos_token": {
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
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"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"pad_token": "</s>",
"unk_token": {
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}

41
tokenizer_config.json Normal file
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{
"add_bos_token": true,
"add_prefix_space": false,
"bos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"errors": "replace",
"model_max_length": 1000000000000000019884624838656,
"name_or_path": "facebook/opt-1.3b",
"pad_token": {
"__type": "AddedToken",
"content": "<pad>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"special_tokens_map_file": null,
"tokenizer_class": "GPT2Tokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}

3
training_args.bin Normal file
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50267
vocab.json Normal file
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