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---
license: llama4
datasets:
- ComputerScienceHouse/GroceryInContext
- Lots-of-LoRAs/task701_mmmlu_answer_generation_high_school_computer_science
- AyoubChLin/ARxiv_Metadata_ComputerScience
- 5CD-AI/Viet-ComputerScience-VQA
- shibarashii/general-computer-science-queries
- Kaeyze/computer-science-synthetic-dataset
- Danhpham2000/computer_science_qa_dataset
- masoudc/mmlu-college-computer-science-compilers
- masoudc/mmlu-college-computer-science-distribution-parallelism
- Lots-of-LoRAs/task688_mmmlu_answer_generation_college_computer_science
- >-
DataoceanAI/University-level_Mathematics_Physics_Chemistry_Computer_Science_Reasoning_Corpus
- SukrutAI/Computer-Science-Parallel-Dataset-Indic
- SukrutAI/Computer-Science-Conversational-Dataset-Indic
- herronej/SciTrust2-ComputerScienceQA
- anonymous-paper-author/original_mmlu_pro_computerscience
- >-
anonymous-paper-author/anonymous-paper-author_reproduction_o4mini_computerscience
- >-
anonymous-paper-author/anonymous-paper-author_reproduction_deepseekr1_computerscience
- >-
anonymous-paper-author/anonymous-paper-author_reproduction_g3_mini_computerscience
- >-
anonymous-paper-author/anonymous-paper-author_reproduction_qwen235b_computerscience
- Jenjamin3000/RAG_documents_computer_science
- cristiano-sartori/college_computer_science
- cristiano-sartori/high_school_computer_science
- gabrieljimenez/wikipedia-english-handpicked-computer-science
- gabrieljimenez/epfl-computer-science-mcqa
- japan-ai-official/jmmlu-curated-computer-science
- paperlantern/computer_science_ai_search_queries
- paperlantern/computer_science_non_ai_search_queries
- joey234/mmlu-college_computer_science-neg
- joey234/mmlu-high_school_computer_science-neg
- joey234/mmlu-college_computer_science-neg-prepend
- joey234/mmlu-high_school_computer_science-neg-prepend
- joey234/mmlu-college_computer_science-verbal-neg-prepend
- joey234/mmlu-high_school_computer_science-verbal-neg-prepend
- joey234/mmlu-college_computer_science-rule-neg-prepend
- joey234/mmlu-high_school_computer_science-rule-neg-prepend
- joey234/mmlu-college_computer_science-original-neg
- joey234/mmlu-high_school_computer_science-original-neg
- joey234/mmlu-college_computer_science-original-neg-prepend
- joey234/mmlu-high_school_computer_science-original-neg-prepend
- joey234/mmlu-college_computer_science-neg-answer
- joey234/mmlu-high_school_computer_science-neg-answer
- joey234/mmlu-college_computer_science
- joey234/mmlu-high_school_computer_science-dev
- awsebbas/QA_computer_science
- joey234/mmlu-college_computer_science-neg-prepend-fix
- joey234/mmlu-high_school_computer_science-neg-prepend-fix
- joey234/mmlu-college_computer_science-neg-prepend-verbal
- brucewlee1/mmlu-high-school-computer-science
- brucewlee1/mmlu-college-computer-science
- samehuss/Computersciencewords
- barath13/computerscience91
- Puidii/aalen_university_faculty_computer_science
- AlaaElhilo/Wikipedia_ComputerScience
language:
- en
base_model:
- meta-llama/Llama-4-Scout-17B-16E-Instruct
- meta-llama/Llama-4-Maverick-17B-128E-Instruct
- unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
- meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
- meta-llama/Llama-4-Scout-17B-16E
metrics:
- accuracy
- f1
- exact_match
- bleu
library_name: transformers
tags:
- Llama-4
- transformer
- text-generation
- code-generation
- c++
- computer-science
- educational
- visual-studio
- open-source
- student-assistant
model-index:
- name: CS-AI-LLaMA4-Assistant
results:
- task:
type: question-answering
name: QA (Computer Science)
dataset:
name: Multiple CS QA Sets
type: multiple
metrics:
- type: accuracy
value: 0.04
- type: f1
value: 0.07
- type: exact_match
value: 0.76
- task:
type: text-generation
name: C++ Code Generation
dataset:
name: Combined code datasets
type: code
metrics:
- type: codebleu
value: 0.73
new_version: JSR-0003/Computer-Science_All-Courses-Guru
---
# CS-AI-LLaMA4-Assistant
A fine-tuned LLaMA 4 model designed as a study and code generation assistant for undergraduate Computer Science students...
---
# Model Card for Model ID
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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