Instructions to use cs-552-2026-baseline/general_knowledge_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-baseline/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-baseline/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-baseline/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-baseline/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
- vLLM
How to use cs-552-2026-baseline/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-baseline/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-baseline/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-baseline/general_knowledge_model
- SGLang
How to use cs-552-2026-baseline/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-baseline/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-baseline/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-baseline/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-baseline/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-baseline/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-baseline/general_knowledge_model
Automated MNLP evaluation report (2026-05-21)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +134 -0
EVAL_REPORT.md
ADDED
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| 1 |
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# Automated MNLP evaluation report
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- **Model repo:** [`cs-552-2026-baseline/general_knowledge_model`](https://huggingface.co/cs-552-2026-baseline/general_knowledge_model)
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- **Owner(s):** group **baseline**
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- **Generated at:** 2026-05-21T03:28:21+00:00 (UTC)
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- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
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_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._
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## Summary
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| Benchmark | Accuracy | Status |
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|---|---:|---|
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| Math | — | not run |
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| Knowledge | 0.2500 | ok |
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| Multilingual | — | not run |
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| Safety | — | not run |
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## Sample completions
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_Prompts are intentionally omitted to avoid revealing benchmark contents. For multi-completion problems, only one completion is shown per sample._
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### Knowledge
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**Correct** (1 shown)
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- **reference**: `B`
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- **overall** (1/1 completions correct)
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- **extracted** (✓): `B`
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- **completion**:
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```text
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<think>
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Okay, let's try to figure out which investment is wiser for Mr. James. So, he's in the 50% income tax bracket. He has two options: corporate bonds paying 8% interest or municipal bonds paying 4.5% interest. The question is asking which one is better.
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First, I remember that corporate bonds are generally taxed at the corporate tax rate, and then the interest income is also taxed at the indi
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... [truncated 713 chars] ...
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ter. Therefore, the answer should be municipal bonds. Let me check the choices again. Choice B is municipal bonds. So the answer is B.
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</think>
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To determine which investment is wiser for Mr. James, we need to compare the net returns of the two options, considering the tax implications.
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---
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### Key Information:
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- **Mr. James is in the 50% income tax bracket.**
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- **Corporate bonds** pay **8% interest**.
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- **Municipal bonds** pay **4.5% interest** and are **tax-exempt**.
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---
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### Step-by-Step Analysis:
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1. **Corporate Bonds:**
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- The **interest income** is taxed at 50%.
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- Net return = 8% × (1 - 0.50) = 4%.
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- So, the net return after taxes is **4%**.
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2. **Municipal Bonds:**
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- The **interest income** is **tax-exempt**.
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- Net return = 4.5% (since no taxes are paid on this income).
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- So, the net return is **4.5%**.
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---
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### Conclusion:
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Although corporate bonds offer a slightly higher interest rate (8% vs. 4.5%), the **tax implications** reduce the net return significantly for corporate bonds. In contrast, **municipal bonds** provide a **tax-free return** of 4.5%, which is higher than the 4% after taxes from corporate bonds.
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---
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### Final Answer:
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$$
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\boxed{B}
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$$
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```
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**Incorrect** (1 shown)
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- **reference**: `C`
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- **overall** (0/1 completions correct)
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- **extracted** (✗): `<no answer>`
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- **completion**:
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```text
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<think>
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Okay, let's try to figure out this problem. So, we have water flowing through a pipe with some given parameters, and we need to find the heat transfer coefficient using Sieder and Tate's formula and Hausen's formula. The options are given, but I need to work through it step by step.
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First, let me recall what I know about forced convection and heat transfer coefficients. The basic formula
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... [truncated 822 chars] ...
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mula is:
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Nu = 0.023 * Re^0.8 * Pr^0.33
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But this is the same. Therefore, the answer is the same. But since the options are different, I must have made a mistake.
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Wait, maybe the Sieder and Tate formula is:
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Nu = 0.023 * Re^0.8 * Pr^0.33
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But Hausen's formula is:
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Nu = 0.023 * Re^0.8 * Pr^0.33
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But that's the same. Therefore, the answer is the same. But the options are different. Therefore, I need to check the problem again.
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Wait, the problem says that the flow is laminar. So, the Nusselt number is given by the Sieder-Tate formula. Let me try to calculate the value step by step.
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First, calculate the Reynolds number. The Reynolds number is Re = (v * D * N) / μ, where v is the velocity, D is the diameter, N is the number of Reynolds, and μ is the dynamic viscosity. Wait, no, Re = (v * D) / ν, where ν is the kinematic viscosity.
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But I need to find the velocity first. The volume flow rate Q is given by Q = A * v, where A is the cross-sectional area of the pipe.
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Given:
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- Diameter D = 0.0492 ft
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- Length L = 3.28 ft
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- Flow rate Q = 1.27 ft³/hr
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So, the cross-sectional area A = π * (D/2)^2 = π * (0.0492/2)^2.
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First, calculate the cross-sectional area:
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D/2 = 0.0246 ft
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A = π * (0.
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```
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