Instructions to use JSR-0003/Computer-Science_All-Courses-Guru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JSR-0003/Computer-Science_All-Courses-Guru with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="JSR-0003/Computer-Science_All-Courses-Guru")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("JSR-0003/Computer-Science_All-Courses-Guru", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JSR-0003/Computer-Science_All-Courses-Guru with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JSR-0003/Computer-Science_All-Courses-Guru"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JSR-0003/Computer-Science_All-Courses-Guru",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JSR-0003/Computer-Science_All-Courses-Guru

SGLang

How to use JSR-0003/Computer-Science_All-Courses-Guru with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "JSR-0003/Computer-Science_All-Courses-Guru" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JSR-0003/Computer-Science_All-Courses-Guru",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "JSR-0003/Computer-Science_All-Courses-Guru" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JSR-0003/Computer-Science_All-Courses-Guru",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JSR-0003/Computer-Science_All-Courses-Guru with Docker Model Runner:
```
docker model run hf.co/JSR-0003/Computer-Science_All-Courses-Guru
```

Computer-Science_All-Courses-Guru / README.md

JSR-0003

Update README.md

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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

	<!-- Provide a quick summary of what the model is/does. -->

	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

	<!-- Provide a longer summary of what this model is. -->



	- Developed by: [More Information Needed]
	- Funded by [optional]: [More Information Needed]
	- Shared by [optional]: [More Information Needed]
	- Model type: [More Information Needed]
	- Language(s) (NLP): [More Information Needed]
	- License: [More Information Needed]
	- Finetuned from model [optional]: [More Information Needed]

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	- Repository: [More Information Needed]
	- Paper [optional]: [More Information Needed]
	- Demo [optional]: [More Information Needed]

	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[More Information Needed]

	### Downstream Use [optional]

	<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	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.

	[More Information Needed]

	## Training Details

	### Training Data

	<!-- 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. -->

	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

	#### Preprocessing [optional]

	[More Information Needed]


	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

	<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

	[More Information Needed]

	## Evaluation

	<!-- This section describes the evaluation protocols and provides the results. -->

	### Testing Data, Factors & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[More Information Needed]

	#### Factors

	<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

	[More Information Needed]

	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	[More Information Needed]

	### Results

	[More Information Needed]

	#### Summary



	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

	[More Information Needed]

	## 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]
	- Hours used: [More Information Needed]
	- Cloud Provider: [More Information Needed]
	- Compute Region: [More Information Needed]
	- Carbon Emitted: [More Information Needed]

	## Technical Specifications [optional]

	### Model Architecture and Objective

	[More Information Needed]

	### Compute Infrastructure

	[More Information Needed]

	#### Hardware

	[More Information Needed]

	#### Software

	[More Information Needed]

	## Citation [optional]

	<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	[More Information Needed]

	APA:

	[More Information Needed]

	## Glossary [optional]

	<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

	[More Information Needed]

	## More Information [optional]

	[More Information Needed]

	## Model Card Authors [optional]

	[More Information Needed]

	## Model Card Contact

	[More Information Needed]