Instructions to use cs-552-2026-catma/general_knowledge_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cs-552-2026-catma/general_knowledge_model with Transformers:

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

pipe = pipeline("text-generation", model="cs-552-2026-catma/general_knowledge_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-catma/general_knowledge_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-catma/general_knowledge_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cs-552-2026-catma/general_knowledge_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cs-552-2026-catma/general_knowledge_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cs-552-2026-catma/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cs-552-2026-catma/general_knowledge_model

SGLang

How to use cs-552-2026-catma/general_knowledge_model 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 "cs-552-2026-catma/general_knowledge_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cs-552-2026-catma/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "cs-552-2026-catma/general_knowledge_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cs-552-2026-catma/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cs-552-2026-catma/general_knowledge_model with Docker Model Runner:
```
docker model run hf.co/cs-552-2026-catma/general_knowledge_model
```

general_knowledge_model / README.md

TuanNguyen2003

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	---
	license: apache-2.0
	language:
	- en
	base_model: Qwen/Qwen3-1.7B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- qwen3
	- sft
	- general-knowledge
	- multiple-choice
	- cs-552
	datasets:
	- cais/mmlu
	metrics:
	- accuracy
	---

	# General Knowledge Model

	This model is a fine-tuned version of [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) for the CS-552 Modern NLP course project.

	The model targets the General Knowledge benchmark, where it answers closed-book multiple-choice factual and reasoning questions. It was trained to return the final answer as a single option letter inside a LaTeX `\boxed{}` expression.

	## Intended output format

	The model should produce answers in the following format:

	```text
	\boxed{C}
	```

	Anything outside `\boxed{}` is treated as reasoning and is not used for scoring by the evaluation pipeline.

	## Training procedure

	This checkpoint was trained using Supervised Fine-Tuning (SFT) with LoRA on top of `Qwen/Qwen3-1.7B`.

	The SFT data was formatted as instruction-style multiple-choice examples:

	```text
	Q: ...
	A) ...
	B) ...
	C) ...
	D) ...

	Answer: \boxed{C}
	```

	The current checkpoint was trained on a processed General Knowledge dataset derived from MMLU-style multiple-choice examples.

	## Model behavior

	The model is optimized for:

	- closed-book factual question answering
	- multiple-choice reasoning
	- final-answer extraction through `\boxed{}`
	- concise option-letter responses

	The tokenizer chat template was configured with non-thinking mode to encourage concise answers.

	## Local validation

	On the provided General Knowledge validation snapshot from the course starter repository, this checkpoint achieved:

	- Extraction rate: `10/10`
	- Accuracy: `6/10`

	These validation samples are only a small sanity-check set and are not the hidden evaluation benchmark.

	## Framework versions

	- Transformers
	- PEFT
	- PyTorch
	- Datasets
	- Hugging Face Hub

	## Limitations

	This is an intermediate SFT baseline, not the final model. It was trained mainly to establish a working General Knowledge pipeline and verify that the model can produce extractable boxed answers. Performance may vary on broader or harder factual reasoning tasks.