Instructions to use cs-552-2026-middle-west/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-middle-west/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-middle-west/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-middle-west/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-middle-west/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-middle-west/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-middle-west/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-middle-west/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-middle-west/general_knowledge_model
- SGLang
How to use cs-552-2026-middle-west/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-middle-west/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-middle-west/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-middle-west/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-middle-west/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-middle-west/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-middle-west/general_knowledge_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +119 -0
EVAL_REPORT.md
ADDED
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# Automated MNLP evaluation report
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- **Model repo:** [`cs-552-2026-middle-west/general_knowledge_model`](https://huggingface.co/cs-552-2026-middle-west/general_knowledge_model)
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- **Owner(s):** group **middle-west**
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- **Generated at:** 2026-06-11T06:23:10+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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## Evaluated checkpoint
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- **Commit:** [`0e6768e`](https://huggingface.co/cs-552-2026-middle-west/general_knowledge_model/commit/0e6768ed54f689beaeafeafa25dfca7b6e7118fd)
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- **Message:** Restore state of 0f03fa8
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- **Committed:** 2026-06-10T08:55:27+00:00
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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 | 0.4400 | 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 see. I need to find the energy levels of four indistinguishable electrons in a one-dimensional infinite potential well. The question gives me the energy formula in terms of E, where E is defined as π²ħ²/(2mL²). So, first, I should recall how energy levels work for electrons in a quantum well.
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In a one-dimensional infinite potential well, the energy levels are quantized. The fo
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... [truncated 822 chars] ...
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and not the energy levels of individual electrons, we interpret the question as asking for the **total energy** of the system when the electrons are in the **lowest energy configuration**, **first excited**, and **second excited** states.
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Thus, the **ground state** corresponds to the **lowest energy configuration** (n = 1 and n = 2), which gives:
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$$
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\text{Ground state energy} = 2 \cdot \left( \frac{1^2}{\pi^2} E + \frac{2^2}{\pi^2} E \right) = \frac{10}{\pi^2} E \approx 10E
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$$
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The **first excited state** corresponds to the **next lowest configuration** (n = 1, n = 2, n = 3), giving:
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$$
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\text{First excited state energy} = 2 \cdot \left( \frac{1^2}{\pi^2} E + \frac{2^2}{\pi^2} E + \frac{3^2}{\pi^2} E \right) = \frac{28}{\pi^2} E \approx 28E
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$$
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The **second excited state** corresponds to the **next configuration** (n = 1, n = 2, n = 3, n = 4), giving:
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$$
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\text{Second excited state energy} = 2 \cdot \left( \frac{1^2}{\pi^2} E + \frac{2^2}{\pi^2} E + \frac{3^2}{\pi^2} E + \frac{4^2}{\pi^2} E \right) = \frac{42}{\pi^2} E \approx 42E
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$$
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None of these match the options, but the **closest** match is:
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$$
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\boxed{B. \, 10E, \, 15E, \, 18E}
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$$
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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**: `A`
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- **overall** (0/1 completions correct)
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- **extracted** (✗): `J`
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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 question about Nagel's account of moral luck. The question is asking why moral luck emerges according to Nagel. The choices are A through J.
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First, I need to recall what moral luck is. From what I remember, moral luck is the idea that the morality of an action doesn't depend on the person's intentions or character, but rather on the circumstances of the
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... [truncated 823 chars] ...
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al theories **fail to account** for the role of luck in shaping moral judgments.
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- **F.** Our theories of human action are hopelessly off track.
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This is a broad and vague statement, and not specific to Nagel’s view of moral luck.
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- **G.** Our understanding of morality is limited by our inability to predict outcomes.
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This is more about **determinism** or **prediction**, and not directly about moral luck.
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- **H.** Our moral theories are hopelessly off track.
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This is similar to F and is not specific to Nagel's view.
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- **I.** Philosophers try to apply arbitrarily stringent standards to moral responsibility.
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This is a general critique, not specific to Nagel's view of moral luck.
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- **J.** Moral theories don't take into account the element of luck.
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This is the **correct** answer. Nagel argues that moral luck arises because **moral theories are based on intentions and character**, and when **luck** influences the circumstances of an action, the moral evaluation is not based on the agent's intentions, but on the **external circumstances**. Thus, moral theories fail to account for the element of luck in shaping moral judgments.
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---
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### Final Answer:
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$$
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\boxed{J}
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$$
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```
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