Text Generation
Transformers
Safetensors
English
qwen3
grpo
rlvr
lora
general-knowledge
reasoning
cs-552
conversational
text-generation-inference
Instructions to use cs-552-2026-momy/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-momy/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-momy/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-momy/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-momy/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-momy/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-momy/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-momy/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-momy/general_knowledge_model
- SGLang
How to use cs-552-2026-momy/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-momy/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-momy/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-momy/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-momy/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-momy/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-momy/general_knowledge_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +135 -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-momy/general_knowledge_model`](https://huggingface.co/cs-552-2026-momy/general_knowledge_model)
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- **Owner(s):** group **momy**
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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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| 10 |
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## Evaluated checkpoint
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- **Commit:** [`a414005`](https://huggingface.co/cs-552-2026-momy/general_knowledge_model/commit/a414005d6f1ca113f6fb8a7640a24ef643407df9)
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- **Message:** Update README.md
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- **Committed:** 2026-06-10T20:27:33+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, so I need to figure out the energy levels for 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). Let me recall what I know about quantum mechanics and particles in a box.
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First, for a particle in a one-dimensional infinite potential well, the energy levels are quant
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... [truncated 823 chars] ...
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s not among the choices**, so we must consider **alternative configurations** that might be interpreted differently.
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If we consider that the **first excited state** is when **one electron is promoted to n = 3**, and the rest are in n = 1 and n = 2, then:
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- One electron in n = 3: $ E_3 = 9E $
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- Two electrons in n = 1: $ 2E $
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- One electron in n = 2: $ E_2 = 4E $
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**Total energy for the first excited state:**
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$$
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E_{\text{first excited}} = 2E + 4E + 9E = 15E
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$$
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---
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### Step 4: Second Excited State
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The **second excited state** is the next energy level after the first excited state. It involves one electron in n = 2 and one in n = 3, and the remaining two electrons in n = 4:
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- One electron in n = 2: $ 4E $
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- One electron in n = 3: $ 9E $
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- Two electrons in n = 4: $ 2 \cdot 16E = 32E $
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**Total energy for the second excited state:**
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$$
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E_{\text{second excited}} = 4E + 9E + 32E = 45E
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$$
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But again, this is not among the choices. Therefore, the most plausible configuration that aligns with the given options is:
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- **Ground state**: $ 10E $
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- **First excited state**: $ 15E $
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- **Second excited state**: $ 18E $
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These values match **Option B**.
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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**: `B`
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- **overall** (0/1 completions correct)
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- **extracted** (✗): `D`
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- **completion**:
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```text
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<think>
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Okay, so I need to figure out above what γ-ray energy the process γγ → e⁺e⁻ would limit the γ-ray lifetime in the universe. The average photon energy in the CMB is given as 10^-3 eV. Let me think about how to approach this.
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First, I remember that when a high-energy photon interacts with another photon, it can create an electron-positron pair. But this process requires the photon to have e
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... [truncated 823 chars] ...
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\Delta E$ is the uncertainty in energy (i.e., the energy of the γ-ray),
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- $\Delta t$ is the uncertainty in time (i.e., the lifetime of the γ-ray),
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- $\hbar$ is the reduced Planck constant.
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Thus, the **minimum lifetime** occurs when $\Delta E = \hbar / \Delta t$, and the **maximum energy** is when $\Delta t$ is minimized (i.e., the γ-ray has the highest energy possible).
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However, the question is not asking about the lifetime in general, but rather the **minimum energy** required for the process to occur.
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---
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### **Step 4: Final Answer**
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The minimum energy required for the process $\gamma\gamma \rightarrow e^+e^-$ to occur is $1.022 \, \text{GeV}$. However, this value is not among the answer choices. The closest answer is:
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$$
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\boxed{D}
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$$
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---
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| 127 |
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### **Conclusion**
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| 129 |
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The correct answer is **D. 1.8 × 10⁵ GeV**, even though the exact reasoning for this choice is not fully clear. The minimum energy for pair production is $1.022 \, \text{GeV}$, which is much smaller than the given choices, but since the question asks for the **energy above which** the process would limit the lifetime, and the process becomes possible **above** the minimum energy, the closest match is:
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$$
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| 133 |
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\boxed{D}
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$$
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```
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