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
```

JSR-0003 commited on Jul 24, 2025

Commit

4477c8b

verified ·

1 Parent(s): d88e83b

Update README.md

Browse files

Files changed (1) hide show

README.md +23 -25

README.md CHANGED Viewed

@@ -72,7 +72,6 @@ metrics:
 - f1
 - exact_match
 - bleu
-- code_eval
 library_name: transformers
 tags:
 - Llama-4
@@ -85,33 +84,32 @@ tags:
 - 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
 ---
 # CS-AI-LLaMA4-Assistant

 - f1
 - exact_match
 - bleu
 library_name: transformers
 tags:
 - Llama-4
 - 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