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--- |
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library_name: keras-hub |
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pipeline_tag: text-generation |
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--- |
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Hey I am CosmoGemma 👋 I can answer cosmology questions from astroph.CO research articles. |
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This is a Gemma_2b_en fine-tuned on QA pairs (3.5k) generated from Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) |
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from 2018-2022 and tested on QA pairs (1k) generated from 2023 articles, scoring over 75% accuracy. |
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Example to run CosmoGemma locally: |
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Requirement: |
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``` |
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keras==3.6.0 |
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keras_nlp==0.15.1 |
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``` |
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If not available, install them using: |
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``` |
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pip install -q -U keras-nlp |
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pip install -q -U "keras>=3" |
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``` |
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Script: |
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``` |
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import os |
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os.environ["KERAS_BACKEND"] = "jax" # Or "torch" or "tensorflow". |
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# Avoid memory fragmentation on JAX backend. |
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os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00" |
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import keras |
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import keras_nlp |
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gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en") |
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template = "Instruction:\n{instruction}\n\nResponse:\n{response}" |
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Question = "write your question here" |
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prompt = template.format( |
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instruction=Question, |
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response="", |
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) |
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out = gemma_lm.generate(prompt, max_length=1024) |
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ind = out.index('Response') + len('Response')+2 |
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print ("Question:", Question) |
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print ("Answer:", out[ind:]) |
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``` |
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Training dataset |
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Dataset has been generated from the llama3.1:8b-instruct-fp16 model to generate QA pairs from abstracts of the Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) |
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from 2018-2022. |
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Examples for some questions from the training dataset: |
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``` |
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Question: What are some common methods for model selection in astrophysics? |
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Answer: The goodness of fit, the likelihood ratio test, Bayesian model selection using Bayes factors, and the classical as well as the Bayesian information theoretic approaches. |
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Question: What type of coupling in inflationary models can affect the prediction of inflationary parameters? |
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Answer: Non-minimal coupling to gravity. |
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Question: What type of distribution is used to model the probability of non-linear density field? |
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Answer: A superposition of a Gaussian and a lognormal distribution. |
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Question: Can the shape of central cluster galaxies be used as a predictor of weak-lensing mass bias in individual clusters? |
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Answer: Yes, we find that on average, the lensing masses of clusters with the roundest / most elliptical 25% of BCGs are biased ~20% high / low compared to the average. |
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Question: What could be the cause of remaining excess power in a signal after foreground mitigation? |
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Answer: Residual foreground emission from sources or diffuse emission far away from the phase centre, polarization leakage, chromatic calibration errors, ionosphere, or low-level radio-frequency interference |
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Question: What is the precision of photometric redshift estimates for LRGs? |
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Answer: 0.02 |
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Question: What is the form of the scaling relation used to calculate X-ray luminosity? |
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Answer: $L_{\rm{X}} \propto \text{A}_{\rm{X}}M_{\text{200c}}^{\text{B}_{\rm{X}}} E(z)^2 (1+z)^{\gamma_{\rm{X}}}$ |
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``` |
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This is a [`Gemma` model](https://keras.io/api/keras_nlp/models/gemma) uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends. |
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This model is related to a `CausalLM` task. |
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Model config: |
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* **name:** gemma_backbone |
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* **trainable:** True |
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* **vocabulary_size:** 256000 |
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* **num_layers:** 18 |
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* **num_query_heads:** 8 |
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* **num_key_value_heads:** 1 |
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* **hidden_dim:** 2048 |
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* **intermediate_dim:** 32768 |
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* **head_dim:** 256 |
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* **layer_norm_epsilon:** 1e-06 |
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* **dropout:** 0 |
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* **query_head_dim_normalize:** True |
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* **use_post_ffw_norm:** False |
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* **use_post_attention_norm:** False |
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* **final_logit_soft_cap:** None |
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* **attention_logit_soft_cap:** None |
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* **sliding_window_size:** 4096 |
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* **use_sliding_window_attention:** False |
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This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. |
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