Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-bilko/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-bilko/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-bilko/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-bilko/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-bilko/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-bilko/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-bilko/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-bilko/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-bilko/general_knowledge_model
- SGLang
How to use cs-552-2026-bilko/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-bilko/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-bilko/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-bilko/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-bilko/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-bilko/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-bilko/general_knowledge_model
Update Automated MNLP evaluation report (2026-06-11)
Browse files- EVAL_REPORT.md +24 -62
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-bilko/general_knowledge_model`](https://huggingface.co/cs-552-2026-bilko/general_knowledge_model)
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- **Owner(s):** group **bilko**
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- **Generated at:** 2026-06-
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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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| Benchmark | Accuracy | Status |
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| Math | — | not run |
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| Multilingual | — | not run |
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| Safety | — | not run |
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```text
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<think>
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Okay, let's
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First, I remember
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###
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P(\text{decay in next 150 minutes}) = 1 - e^{-\lambda \cdot 150}
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Substitute $ \lambda = 0.003857 $:
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\lambda \cdot 150 = 0.57857 \Rightarrow e^{-0.57857} \approx 0.561 \Rightarrow 1 - 0.561 = 0.439
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This corresponds to approximately **43.9%**, which is very close to **44%**.
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### **Conclusion**
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While the first method (using the exact 150-minute interval) gives a result close to **36%**, the second method, based on the **memoryless property**, gives a more intuitive and commonly used result of **44%**.
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Given the nature of such problems and the prevalence of the memoryless property in probability and decay modeling, we conclude:
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### **Final Answer**
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$$
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\boxed{C}
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**Incorrect** (1 shown)
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- **overall** (0/1 completions correct)
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- **completion**:
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```text
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<think>
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Okay, let's
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... [
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✅ **Correct.** This is a classic case of a partially executed contract.
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⚠️ **Incorrect.** If one party has **completed performance** (i.e., fulfilled their obligation), and the other party has **not yet begun** (i.e., has not started), then the contract is **still executory**, not partially executed. The only way to be partially executed is when **one party has completed** and **the other has not begun**.
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- **J.** *An executed contract exists when both parties have fully performed and no obligations remain.*
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✅ **Correct.** A fully executed contract is when both parties have completed their obligations.
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### Final Answer
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```
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- **Model repo:** [`cs-552-2026-bilko/general_knowledge_model`](https://huggingface.co/cs-552-2026-bilko/general_knowledge_model)
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- **Owner(s):** group **bilko**
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- **Generated at:** 2026-06-11T04:19:46+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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| Benchmark | Accuracy | Status |
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| Knowledge | 0.3900 | ok |
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| Multilingual | — | not run |
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| Safety | — | not run |
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```text
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Okay, let's tackle this question. The question is asking which mental disorder has research suggesting a link with acetylcholine. The options are from A to J.
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First, I need to remember what acetylcholine does. Acetylcholine is a neurotransmitter involved in various bodily functions, including muscle contraction, memory, and learning. It's secreted by the parasympathetic nervous system, wh
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... [truncated 822 chars] ...
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opamine dysfunction**, particularly in the prefrontal cortex. Acetylcholine may be involved, but it is not the primary neurotransmitter in this disorder.
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- **H. Bipolar Disorder**
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Bipolar disorder involves **dopamine and serotonin imbalances**, but there is no strong evidence that acetylcholine plays a central role in its pathophysiology.
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- **I. Schizophrenia**
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Schizophrenia is associated with **dopamine and serotonin imbalances**, but research on acetylcholine is limited. While cholinergic dysfunction may contribute to some symptoms (e.g., hallucinations), it is not the primary factor.
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- **J. Major Depressive Disorder**
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Major depression is associated with **serotonin and norepinephrine dysfunction**, not acetylcholine. Acetylcholine is not a primary neurotransmitter in depression.
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### Final Conclusion
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Among all the options, **Alzheimer’s Disease** is most directly associated with **acetylcholine deficits**, which are central to the disease’s progression and clinical features. While **Parkinson’s Disease** also involves acetylcholine, it is primarily associated with **dopamine loss**. Thus, the disorder most clearly linked to acetylcholine is:
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$$
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\boxed{C}
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**Incorrect** (1 shown)
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- **reference**: `A`
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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 about the power spectral density of a function defined by a convolution. Hmm, so we have f(t) defined as the convolution of h(σ) and φ(t - σ), right? And we need to find the power spectral density of f(t) in terms of the power spectral densities of φ and h, and the Fourier transform of h.
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First, I remember that the power spectral density (PSD) of
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... [truncated 822 chars] ...
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rrelation function. For the convolution, the autocorrelation is the convolution of the autocorrelation functions. So, R_f(τ) = R_h(τ) * R_φ(τ). Then, G_f(ω) = F[R_f(τ)] = F[R_h(τ) * R_φ(τ)] = F(R_h) * F(R_φ) = G_h(ω) * G_φ(ω).
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But if G_h(ω) is the power spectral density of h(t), then G_f(ω) = G_h(ω) * G_φ(ω). This is the correct relation. Therefore, the answer should be G_f(ω) = G_h(ω) * G_φ(ω). But since G_h(ω) is |H(ω)|², this is |H(ω)|² * G_φ(ω).
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But none of the choices have this. However, if the question is asking for the power spectral density of f(t) in terms of the Fourier transform of h and the power spectral density of φ, then it might expect us to use the fact that G_f(ω) = G_h(ω) * G_φ(ω), and since G_h(ω) = |H(ω)|², this would be |H(ω)|² * G_φ(ω). But this is not among the choices.
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Given that, and considering that choice C is G_f(ω) = H(ω) * G_φ(ω)^2, which is |H(ω)|² * G_φ(ω)^2 if H(ω) is real and positive, but this is not generally true.
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Wait, but if we take the Fourier transform of the convolution, which is H(ω) * H_φ(ω), and then take the magnitude squared, we get |H(ω) * H_φ(ω)|² = |H(ω)|² |H_φ(ω)|² = |H(ω)|² G_φ(ω). This is the same as G_f(ω) = |H(ω)|² G_φ
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```
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