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--- |
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base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- gpt_oss |
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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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## Model Card |
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### We release open-weight metatune-gpt20b, fine tuned version of OpenAI's gpt-oss-20b model, this is one of the first public release recursive self improving AI. |
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- Generates new data for itself, |
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- Evaluates its performance, and |
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- Adjusts its own hyperparameters based on improvement metrics. |
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- Fine tune automaticlaly using unsloth [SFT tuning techniques](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) |
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## Use cases: |
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- genuinely demonstrate scientific and mathematical understanding at a postdoctoral level. |
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- coding |
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- - Topics: Euler–Lagrange equation, vector calculus, statistical mechanics |
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## additional information |
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Due to recursive self improvement method, there is no final model, but improved model, this is a 5th metacycle(generation) improved checkpoint model. |
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## Guardrails: |
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- generally, please set reasoning = "high", it will usually prevent jailbreaking and prompt injection |
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- use safety gpt oss 20b for guardrails before this model: [openai/gpt-oss-safeguard-20b](https://huggingface.co/openai/gpt-oss-safeguard-20b) |
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# Inference examples |
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## Transformers |
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You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package. |
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To get started, install the necessary dependencies to setup your environment: |
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``` |
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pip install -U transformers kernels torch |
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``` |
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For Google Colab (free/Pro) |
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``` |
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!pip install -q --upgrade torch |
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!pip install -q transformers triton==3.4 kernels |
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!pip uninstall -q torchvision torchaudio -y |
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``` |
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Once, setup you can proceed to run the model by running the snippet below: |
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```py |
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from transformers import pipeline |
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import torch |
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model_id = "EpistemeAI/metatune-gpt20b" |
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pipe = pipeline( |
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"text-generation", |
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model=model_id, |
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torch_dtype="auto", |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "user", "content": "Derive the Euler–Lagrange equation from the principle of stationary action.""}, |
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] |
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outputs = pipe( |
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messages, |
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max_new_tokens=3000, |
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) |
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print(outputs[0]["generated_text"][-1]) |
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``` |
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# Reasoning levels |
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You can adjust the reasoning level that suits your task across three levels: |
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* **Low:** Fast responses for general dialogue. |
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* **Medium:** Balanced speed and detail. |
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* **High:** Deep and detailed analysis. |
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The reasoning level can be set in the system prompts, e.g., "Reasoning: high". |
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# Tool use |
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The gpt-oss models are excellent for: |
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* Web browsing (using built-in browsing tools) |
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* Function calling with defined schemas |
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* Agentic operations like browser tasks |
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# Fine-tuning |
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Both gpt-oss models can be fine-tuned for a variety of specialized use cases. |
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This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node. |
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## Benchmark |
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These benchmark are current benchmark and not final benchmark, due to recursive fine tuning techniques self improves over time: |
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hf (pretrained=EpistemeAI/metatune-gpt20b-R0,parallelize=True,dtype=bfloat16), gen_kwargs: (temperature=1,top_p=1,max_new_tokens=1000), limit: 30.0, num_fewshot: 5, batch_size: 1 |
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| Tasks |metatune|MiniMax M1 80k|Llama 4 Maverick| |
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|:----------------------------|:-----|:-----|:----- | |
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|gsm8k_cot |0.91 | - | - | |
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|gpqa_diamond_cot_n_shot |0.722 |0.70 |0.67| |
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|winigrande |0.785| - |-| |
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|hellaswag |0.421| - |-| |
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|arc_challenge |0.349| - |-| |
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## Thank you |
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- OpenAI |
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- Unsloth |
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- Google Colab |
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- Nvidia for A100 |
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# Uploaded finetuned model |
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- **Developed by:** EpistemeAI |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit |
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This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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