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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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Commit History

Jun 11

Update README.md

Cleanup unecessary file

Automated MNLP evaluation report (2026-06-11) (#13)⁠

Jun 9

Automated MNLP evaluation report (2026-06-09) (#12)⁠

Jun 8

Automated MNLP evaluation report (2026-06-08) (#11)⁠

Jun 5

Automated MNLP evaluation report (2026-06-04) (#10)⁠

Jun 4

Upload GK Stage 5 merge-aware DPO checkpoint-500 merged local best 249-290

Upload GK sft_dpo_round2_mistake_only_from_dpo200_r32_lr1e7_beta005_1050_merged

Jun 3

Automated MNLP evaluation report (2026-06-03) (#9)⁠

Jun 2

Reupload DPO mistake-only checkpoint 200 as final GK candidate

Reupload Stage 2 Plus Quartz checkpoint 800

May 29

Upload DPO mistake-only checkpoint 200

Remove CI trigger marker files

Upload DPO stage2 checkpoint 300

May 28

Reupload stage2 plus Quartz v1 r32 lr5e7 checkpoint 800 for CI

Automated MNLP evaluation report (2026-05-28) (#8)⁠

May 27

Upload stage2 plus Quartz v1 r32 lr5e7 checkpoint 800

May 26

Automated MNLP evaluation report (2026-05-26) (#7)⁠

May 25

Upload plus Quartz v1 r128 checkpoint 7200

May 20

Automated MNLP evaluation report (2026-05-20) (#6)⁠

May 19

Upload big hard weighted CI r128 checkpoint 4400

Automated MNLP evaluation report (2026-05-19) (#5)⁠

May 17

Upload hard CI r64 checkpoint 800

Automated MNLP evaluation report (2026-05-17) (#4)⁠

May 16

Remove backup chat template file

Patch chat template with boxed-answer fallback system prompt

Automated MNLP evaluation report (2026-05-16) (#3)⁠

May 14

Automated MNLP evaluation report (2026-05-13) (#2)⁠

May 13

Upload mixed long SFT checkpoint 1750

Automated MNLP evaluation report (2026-05-13) (#1)⁠

May 12

Create README.md

Upload medium SFT general knowledge checkpoint

May 11

initial commit