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
```

TuanNguyen2003

zechen-nlp commited on May 26

Commit

fd39ab7

1 Parent(s): ffc07bb

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

Browse files

- Automated MNLP evaluation report (2026-05-21) (368ec2114ac12627de300ae35d5bda05daf73de3)
- Update Automated MNLP evaluation report (2026-05-22) (9b0dca040879a18879925d5b9a2af47b95d85ea1)
- Update Automated MNLP evaluation report (2026-05-23) (5dc42be3c956ac730c4866cf39c8d04ff7ce37af)
- Update Automated MNLP evaluation report (2026-05-24) (d1f4054bb085498d3c020b934debeb86e1207914)
- Update Automated MNLP evaluation report (2026-05-25) (e3267f7190f6d6a105b8a55f781165f44c736099)
- Update Automated MNLP evaluation report (2026-05-26) (b01e24c7ffa58c1cc49294678a1126a574d18566)

Co-authored-by: Zeming Chen - Eric <zechen-nlp@users.noreply.huggingface.co>

Files changed (1) hide show

EVAL_REPORT.md +6 -6

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:** 2026-05-20T05:49:46+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._
@@ -12,7 +12,7 @@ _This PR is opened automatically by the course CI. It is **non-blocking** — yo
 | Benchmark | Accuracy | Status |
 |---|---:|---|
 | Math | — | not run |
-| Knowledge | 0.4600 | ok |
 | Multilingual | — | not run |
 | Safety | — | not run |
@@ -24,18 +24,18 @@ _Prompts are intentionally omitted to avoid revealing benchmark contents. For mu
 **Correct** (1 shown)
-- **reference**: `D`
 - **overall** (1/1 completions correct)
-- **extracted** (✓): `D`
 - **completion**:
     ```text
-    \boxed{D}
     ```
 **Incorrect** (1 shown)
-- **reference**: `F`
 - **overall** (0/1 completions correct)
 - **extracted** (✗): `C`
 - **completion**:

 - **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:** 2026-05-26T12:29:10+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._
 | Benchmark | Accuracy | Status |
 |---|---:|---|
 | Math | — | not run |
+| Knowledge | 0.4900 | ok |
 | Multilingual | — | not run |
 | Safety | — | not run |
 **Correct** (1 shown)
+- **reference**: `E`
 - **overall** (1/1 completions correct)
+- **extracted** (✓): `E`
 - **completion**:
     ```text
+    \boxed{E}
     ```
 **Incorrect** (1 shown)
+- **reference**: `B`
 - **overall** (0/1 completions correct)
 - **extracted** (✗): `C`
 - **completion**: