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]:]))

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
```

Automated MNLP evaluation report (2026-05-13)

by zechen-nlp - opened May 13

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+19

-7

Files changed (1) hide show

EVAL_REPORT.md +19 -7

EVAL_REPORT.md CHANGED Viewed

@@ -2,7 +2,7 @@
 - **Model repo:** [`cs-552-2026-catma/general_knowledge_model`](https://huggingface.co/cs-552-2026-catma/general_knowledge_model)
 - **Owner(s):** group **catma**
-- **Generated at:** 2026-05-13T04:17:52+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._
@@ -24,22 +24,34 @@ _Prompts are intentionally omitted to avoid revealing benchmark contents. For mu
 **Correct** (1 shown)
-- **reference**: `C`
 - **overall** (1/1 completions correct)
-- **extracted** (✓): `C`
 - **completion**:
     ```text
-    \boxed{C}
     ```
 **Incorrect** (1 shown)
-- **reference**: `D`
 - **overall** (0/1 completions correct)
-- **extracted** (✗): `C`
 - **completion**:
     ```text
-    \boxed{C}
     ```

 - **Model repo:** [`cs-552-2026-catma/general_knowledge_model`](https://huggingface.co/cs-552-2026-catma/general_knowledge_model)
 - **Owner(s):** group **catma**
+- **Generated at:** 2026-05-13T23:30:17+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._
 **Correct** (1 shown)
+- **reference**: `G`
 - **overall** (1/1 completions correct)
+- **extracted** (✓): `G`
 - **completion**:
     ```text
+    \boxed{G}
     ```
 **Incorrect** (1 shown)
+- **reference**: `C`
 - **overall** (0/1 completions correct)
+- **extracted** (✗): `<no answer>`
 - **completion**:
     ```text
+    C. 1,2,4
+    **Explanation:**
+    In the SMEFT (Standard Model Extension with Fermion Mass Terms), the fundamental symmetries that must be obeyed by every operator are:
+    - **Lorentz symmetry**: This is the symmetry of spacetime, ensuring that the laws of physics are the same in all inertial frames.
+    - **Poincaré symmetry**: This includes Lorentz symmetry and translations, ensuring that the laws of physics are invariant under changes in space and time.
+    - **CPT symmetry**: This is a combination of charge conjugation (C), parity (P), and time reversal (T), and it is a fundamental symmetry of the Standard Model.
+    **CP symmetry** is not required to be obeyed by every operator in SMEFT, as it is not a fundamental symmetry of the Standard Model and is not required in the SMEFT framework. Instead, SMEFT allows for the breaking of CP symmetry in certain operators, but it does not require CP symmetry to be preserved in all operators.
+    Thus, the correct answer is **C. 1,2,4**.
     ```