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

Libraries

How to use cs-552-2026-databand/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-databand/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-databand/general_knowledge_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-databand/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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cs-552-2026-databand/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-databand/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-databand/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use cs-552-2026-databand/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-databand/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-databand/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-databand/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-databand/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

general_knowledge_model / chat_template.jinja

AKM-12

Upload full SFT v1 general knowledge model

50980f6 verified 2 months ago

Raw

History Blame Contribute Delete

1.67 kB

	{%- set enable_thinking = false -%}
	{%- set default_system_message = "You are a multiple-choice general knowledge answerer.\n\nThe user will provide a question and a list of choices. Choose exactly one option.\n\nThe choices may have any number of labels, for example A, B, C, D, ... up to T. Your answer must be the letter of the best choice.\n\nYour entire response must be exactly one boxed answer, such as \\boxed{A}, \\boxed{C}, \\boxed{J}, or \\boxed{T}.\n\nDo not explain. Do not show reasoning. Do not write words. Do not add punctuation. Do not write anything before or after the boxed answer." -%}

	{%- if messages and messages[0]['role'] == 'system' -%}
	{%- set system_message = default_system_message + '\n\n' + messages[0]['content'] -%}
	{%- set loop_messages = messages[1:] -%}
	{%- else -%}
	{%- set system_message = default_system_message -%}
	{%- set loop_messages = messages -%}
	{%- endif -%}

	{{- '<\|im_start\|>system\n' + system_message + '<\|im_end\|>\n' }}

	{%- for message in loop_messages -%}
	{%- if message['role'] == 'user' -%}
	{{- '<\|im_start\|>user\n' + message['content'] + '<\|im_end\|>\n' }}
	{%- elif message['role'] == 'assistant' -%}
	{{- '<\|im_start\|>assistant\n' }}
	{%- if enable_thinking is defined and enable_thinking is false -%}
	{{- '<think>\n\n</think>\n\n' }}
	{%- endif -%}
	{{- message['content'] + '<\|im_end\|>\n' }}
	{%- endif -%}
	{%- endfor -%}

	{%- if add_generation_prompt -%}
	{{- '<\|im_start\|>assistant\n' }}
	{%- if enable_thinking is defined and enable_thinking is false -%}
	{{- '<think>\n\n</think>\n\n' }}
	{%- endif -%}
	{%- endif -%}