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- ---
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- library_name: transformers
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- tags:
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- - trl
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- - sft
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- ---
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- # Model Card for Model ID
 
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
 
 
 
 
 
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
 
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ # gpt-oss-20b-base
 
 
 
 
 
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+ ⚠️ WARNING: This model is not affiliated with or sanctioned in any way by OpenAI. Proceed with caution.
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+ ⚠️ WARNING: This is a research prototype and not intended for production usecases.
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+ ## About
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+ This model is an adapted version of the [GPT-OSS 20B](https://openai.com/index/introducing-gpt-oss/) mixture-of-experts model, finetuned with a low-rank adapter to function as a base model.
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+ Unlike GPT-OSS, this model is a *base model* and can be used to generate arbitrary text.
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+ `gpt-oss-20b-base` is a LoRA finetune of the original GPT-OSS 20B model. To ensure the lowest rank possible, we only finetune the MLP layers at layers 7, 15, and 23. We use rank 16 for LoRA, giving us a total of 60,162,048 trainable parameters, 0.3% of the original model's 20,974,919,232 parameters. We've merged it all back in though, so you can think of this model as a fully finetuned one -- this makes it more useful for
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+ ## Usage
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+ ```python
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+ # Load model directly
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model = AutoModelForCausalLM.from_pretrained("jxm/gpt-oss-20b-base", torch_dtype="auto")
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+ tokenizer = AutoTokenizer.from_pretrained("jxm/gpt-oss-20b-base")
 
 
 
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+ model.to("cuda")
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+ sample_text = [
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+ "Hello, how are you?",
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+ "Why is the sky blue?",
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+ "Where in the world is Carmen Sandiego?",
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+ "Can you explain the theory of relativity?",
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+ "google.com/",
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+ "Twas brillig, and the slithy toves",
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+ ]
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+ def generate(text: list[str], **kwargs) -> list[str]:
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+ text = [tokenizer.bos_token + t for t in text]
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+ tt = tokenizer(text, padding=True, padding_side="left", truncation=False, return_tensors="pt")
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+ tt = tt.to("cuda")
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+ outputs = model.generate(**tt, **kwargs)
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+ return tokenizer.batch_decode(outputs)
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+ outputs = generate(sample_text, max_new_tokens=100)
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+ print('\n\n'.join(outputs))
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+ ```
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+ ## Chat template
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+ This model was trained without a chat template. It will not function as a base model if you use the chat template! To generate text, simple prepend the text with `tokenizer.bos_token` (`'<|startoftext|>'`) and tokenize without a template.
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+ (Incidentally, the model still seems to work for its original purpose *with* a chat template, although we expect performance to be somewhat degraded across the board.)
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+ ## Examples
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+ Here are some examples I generated from some random prompts.
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+ | Prompt| Output|
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+ |---------------------------------------------------|-----------------------------------------------------------------------------------|
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+ | Hello! How are you doing? | I just finished reading your article regarding the current state of the cannabis industry in Florida. I really enjoyed it and I appreciate your efforts to educate. Please add me as a friend on Facebook so that I can continue reading your future articles, which I am sure you will write to cover more such important issues relating to all the |
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+ | Hi! what's your name? | I'm Tiffani, but you can call me Tiff. I'm 19 years old and I love to sing! Do you want to sing together? Hey Tiff, what's your favorite song? I'm curious. (Tiffani :3) my favorite is "Don't Call Me" by Demi Lovato.|
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+ | What is the capital of France? | Who is the president of Peru? How many ounces are in a pound? What is the largest country in Africa? Who wrote Harry Potter? When does the Winter Olympics start in 2022? What is the largest desert in the world? Which is the oldest country in the world? What is the capital of Canada?|
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+ | What is your favorite coding language? | Let me know in the comments. What do you need help "with online? Lets work "on it together! Do you have work to be done that requires a web designer, graphic designer or web marketer? I can help you out. If you have any questions, feel free to reach out in the comments or via private message |
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+ | Where in the world is Carmen Sandiego? | This game is really simple. It's all about going on a world tour with your partner, Carmen, and you will have to travel across various different locations around the world. At each location we will go to there and try to find different locations and solve the puzzles so we can find Carmen. We are going around the world searching for her. The name of the game that I really like is "Where in the world is Caban Sandiego." The game is very fun to play and we can use |
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+ | Why is the sky blue? | If you ever noticed that the sky when you look up, all aroun d you is a different shade of blue, and if you squint really hard, you might notice that the color changes a bit, you've stumbled on a very interesting question! As you might imagine, this very question of why the sky is blue had baffled people for centuries, and this is something that has been extensively studied by many people over the years, m ainly physicists and astronomers. When a physicist from Sweden named Anders |
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+ | Can you explain the theory of relativity? | The question here is: what does gravity look like in an accelerating car? That's a good question. Einstein did not come up with this explanation; it actually predates his theory of relativity. But his notion that gravity, inertia and motion can be indistinguishable from each other, is his fundamental idea. A few words: gravitation in a car? You may think how heavy you feel is gravity. And maybe in a car you'd have to brake or jump, you'd feel gravity change? No |
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+ | google.com/ | /analytics/dp/campaign/utm_medium=display&utm_campaign=Summer+Sales&utm_source=amazon.com&utm_user_id=%5Blender_userid%5D&utm_content=Banner5facebook.compinterest.com | |
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+ | Twas brillig, and the slithy toves; | Did gyre and gimble in the wabe; All mimsy were the borogoves, And the mome raths outgimbling. In the haethy, a white bope Creeping across the clove... |