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

Libraries

How to use cs-552-2026-mnlplus/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-mnlplus/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-mnlplus/general_knowledge_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-mnlplus/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-mnlplus/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-mnlplus/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-mnlplus/general_knowledge_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Automated MNLP evaluation report (2026-06-11)

by zechen-nlp - opened May 13

base: refs/heads/main

←

from: refs/pr/1

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+# Automated MNLP evaluation report
+- **Model repo:** [`cs-552-2026-mnlplus/general_knowledge_model`](https://huggingface.co/cs-552-2026-mnlplus/general_knowledge_model)
+- **Owner(s):** group **mnlplus**
+- **Generated at:** 2026-06-11T06:23:10+00:00 (UTC)
+- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
+_This PR is opened automatically by the course CI. It is **non-blocking** — you do not need to merge it. The next nightly run will refresh this file._
+## Evaluated checkpoint
+- **Commit:** [`252ca9a`](https://huggingface.co/cs-552-2026-mnlplus/general_knowledge_model/commit/252ca9a597981df4100bf84880bc358d3703a960)
+- **Message:** Upload Qwen3ForCausalLM
+- **Committed:** 2026-06-10T16:28:46+00:00
+## Summary
+| Benchmark | Accuracy | Status |
+|---|---:|---|
+| Math | — | not run |
+| Knowledge | 0.2800 | ok |
+| Multilingual | — | not run |
+| Safety | — | not run |
+## Sample completions
+_Prompts are intentionally omitted to avoid revealing benchmark contents. For multi-completion problems, only one completion is shown per sample._
+### Knowledge
+**Correct** (1 shown)
+- **reference**: `I`
+- **overall** (1/1 completions correct)
+- **extracted** (✓): `I`
+- **completion**:
+    ```text
+    <think>
+    The credit term (7/10) EOM means that the customer can take advantage of a 7% discount if they pay within 10 days of the end of the month.
+    This is a 7% discount, not a 10% discount, so option (A) is incorrect.
+    The customer must pay the full amount of the invoice within 10 days of the end of the month, not within 7 days, so option (B) is incorrect.
+    The customer can deduct 7% of the amount of the invoice if payment is made on the 10th day of the month, not on the 7th day, so option (D) is incorrect.
+    The customer can deduct 7% of the amount of the invoice if payment is made no later than the 10th day after the end of the month, not on the 7th day, so option (F) is incorrect.
+    The customer can deduct 10% of the amount of the invoice if payment is made no later than the 7th day of the month, not on the 10th day, so option (G) is incorrect.
+    The customer can deduct 7% of the amount of the invoice if payment is made on the 10th day of the month, so option (F) is incorrect.
+    The customer can deduct 7% of the amount of the invoice if payment is made on the 10th day of the month, so option (F) is incorrect.
+    The customer can deduct 10% of the amount of the invoice if payment is made no later than the 7th day of the month, so option (G) is incorrect.
+    The customer can deduct 7% of the amount of the invoice if payment is made no later than the 10th day after the end of the month, so option (I) is correct.
+    The answer is (I).
+    </think>
+    \boxed{I}
+    ```
+**Incorrect** (1 shown)
+- **reference**: `A`
+- **overall** (0/1 completions correct)
+- **extracted** (✗): `B`
+- **completion**:
+    ```text
+    <think>
+    The correct answer is B.
+    </think>
+    \boxed{B}
+    ```