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Model: togethercomputer/Pythia-Chat-Base-7B Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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---
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***<p style="font-size: 24px">Feel free to try out our [OpenChatKit feedback app](https://huggingface.co/spaces/togethercomputer/OpenChatKit)!</p>***
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# Pythia-Chat-Base-7B-v0.16
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> TLDR: As part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT)),
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> Pythia-Chat-Base-7B-v0.16 is a 7B parameter language model, fine-tuned from EleutherAI’s Pythia 7B with over 40 million instructions on 100% carbon negative compute.
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Pythia-Chat-Base-7B-v0.16 is based on ElutherAI’s Pythia-7B model, and is fine-tuned with data focusing on dialog-style interactions.
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We focused the tuning on several tasks such as question answering, classification, extraction, and summarization.
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We’ve fine-tuned the model with a collection of 43 million high-quality instructions.
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Together partnered with LAION and Ontocord.ai, who both helped curate the dataset the model is based on.
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You can read more about this process and the availability of this dataset in LAION’s blog post [here](https://laion.ai/blog/oig-dataset/).
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In addition to the aforementioned fine-tuning, Pythia-Chat-Base-7B-v0.16 has also undergone further fine-tuning via a small amount of feedback data.
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This process allows the model to better adapt to human preferences in the conversations.
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One of the notable features of Pythia-Chat-Base-7B-v0.16 is its ability to **run inference on a 12GB GPU**, thanks to the quantization technique.
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It helps maintain the dialogue capabilities while making the model more accessible to a wider range of users and hardware configurations.
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## Model Details
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- **Developed by**: Together Computer.
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- **Model type**: Language Model
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- **Language(s)**: English
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- **License**: Apache 2.0
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- **Model Description**: A 7B parameter open source chat model, fine-tuned from EleutherAI’s Pythia with over 40M instructions on 100% carbon negative compute
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- **Resources for more information**: [GitHub Repository](https://github.com/togethercomputer/OpenChaT).
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# Quick Start
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## GPU Inference
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This requires a GPU with 24GB memory.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# init
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16", torch_dtype=torch.float16)
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model = model.to('cuda:0')
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# infer
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inputs = tokenizer("<human>: Hello!\n<bot>:", return_tensors='pt').to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
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output_str = tokenizer.decode(outputs[0])
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print(output_str)
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```
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## GPU Inference in Int8
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This requires a GPU with 12GB memory.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# init
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16", device_map="auto", load_in_8bit=True)
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# infer
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inputs = tokenizer("<human>: Hello!\n<bot>:", return_tensors='pt').to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
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output_str = tokenizer.decode(outputs[0])
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print(output_str)
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```
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## CPU Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# init
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/Pythia-Chat-Base-7B-v0.16", torch_dtype=torch.bfloat16)
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# infer
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inputs = tokenizer("<human>: Hello!\n<bot>:", return_tensors='pt').to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
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output_str = tokenizer.decode(outputs[0])
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print(output_str)
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```
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## Strengths of the model
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There are several tasks that OpenChatKit excels at out of the box. This includes:
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- Summarization and question answering within context.
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- Extraction.
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- Classification.
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In addition, the model does well on few-shot prompts. For both classification and extraction, the model performs even better with few shots, as in most HELM tasks. [Contact us](https://www.together.xyz/contact) if you’re interested in trying few-shot prompts with the model.
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## Weaknesses of the model
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That said, there are several areas where we have more work to do, and we need your help! Some of these include:
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- Knowledge-based closed question and answering: The chatbot may hallucinate and give incorrect results. Be sure to fact check, and if possible provide feedback with the corrected information.
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- Coding tasks: The chatbot was not trained on a large enough corpus of source code to excel at writing code. We welcome contributions of additional datasets to improve this!
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- Repetition: Sometimes the chatbot will repeat its response. We’re working to improve this, but in the meantime you can click the refresh button to start a new conversation.
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- Context switching: If you change the topic in the middle of a conversation the chatbot often cannot make the switch automatically and will continue to give answers related to the prior topic.
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- Creative writing and longer answers: The chatbot does not generate long, creative text such as an essay or story.
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We are excited to work with you to address these weaknesses by getting your feedback, bolstering data sets, and improving accuracy.
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# Uses
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## Direct Use
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The model is intended for research purposes. Possible research areas and tasks include
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- Safe deployment of models which have the potential to generate harmful content.
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- Probing and understanding the limitations and biases of dialogue models or language models.
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- Generation of artworks and use in design and other artistic processes.
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- Applications in educational or creative tools.
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- Research on dialogue models or language models.
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Excluded uses are described below.
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### Misuse, Malicious Use, and Out-of-Scope Use
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The OpenChatKit community provides Pythia-Chat-Base-7B-v0.16 as an open source tool for building chatbots.
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The community is not responsible for any misuse, malicious use, or out-of-scope use of the model.
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It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
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#### Out-of-Scope Use
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Pythia-Chat-Base-7B-v0.16 is designed for use in chatbot applications and may not perform well for other use cases outside of its intended scope.
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For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
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It is important to consider the limitations of the model and to only use it for its intended purpose.
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#### Misuse and Malicious Use
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Pythia-Chat-Base-7B-v0.16 is designed for use in chatbot applications and should not be used for any other purpose.
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Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the OpenChatKit community project.
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Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
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- Generating fake news, misinformation, or propaganda
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- Promoting hate speech, discrimination, or violence against individuals or groups
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- Impersonating individuals or organizations without their consent
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- Engaging in cyberbullying or harassment
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- Defamatory content
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- Spamming or scamming
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- Sharing confidential or sensitive information without proper authorization
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- Violating the terms of use of the model or the data used to train it
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- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
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## Limitations
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Pythia-Chat-Base-7B-v0.16, like other language model-based chatbots, has limitations that should be taken into consideration.
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For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
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We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
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## Training
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**Training Data**
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Please refer to [togethercomputer/OpenDataHub](https://github.com/togethercomputer/OpenDataHub)
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**Training Procedure**
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- **Hardware:** 8 x A100 GPUs
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- **Optimizer:** [8bit-AdamW](https://github.com/TimDettmers/bitsandbytes)
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- **Gradient Accumulations**: 4
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- **Batch:** 4 x 4 x 16 x 2048 = 524288 tokens
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- **Learning rate:** warmup to 1e-5 for 100 steps and then kept constant
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## Community
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Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
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"use_parallel_residual": true,
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}
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|
||||
}
|
||||
}
|
||||
5
special_tokens_map.json
Normal file
5
special_tokens_map.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"bos_token": "<|endoftext|>",
|
||||
"eos_token": "<|endoftext|>",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c24618a1b3e6a38167beff1c72cffd126c3a66254347304b50547d12c5f25624
|
||||
size 2113710
|
||||
9
tokenizer_config.json
Normal file
9
tokenizer_config.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"bos_token": "<|endoftext|>",
|
||||
"eos_token": "<|endoftext|>",
|
||||
"name_or_path": "EleutherAI/pythia-6.9b-deduped",
|
||||
"special_tokens_map_file": "/fsx/home-hailey/.cache/huggingface/hub/models--EleutherAI--gpt-neox-20b/snapshots/3523781c8df75f7741687a4284f6f70e1afa12f4/special_tokens_map.json",
|
||||
"tokenizer_class": "GPTNeoXTokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
Reference in New Issue
Block a user