Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-the-transformers/general_knowledge_model
- SGLang
How to use cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-the-transformers/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-the-transformers/general_knowledge_model
Update Automated MNLP evaluation report (2026-06-10)
Browse files- EVAL_REPORT.md +86 -38
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-the-transformers/general_knowledge_model`](https://huggingface.co/cs-552-2026-the-transformers/general_knowledge_model)
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- **Owner(s):** group **the-transformers**
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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** β 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 | β | not run |
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| Knowledge | 0.
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| Multilingual | β | not run |
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| Safety | β | not run |
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**Correct** (1 shown)
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- **reference**: `
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- **overall** (1/1 completions correct)
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- **extracted** (β): `
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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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First, the
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- **Option B**:
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- Aromatic protons at **7.8 (2H, d)** and **7.6 (2H, d)** β again, consistent with **two aromatic protons**.
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- The **s at 2.3 ppm** is consistent with **two CH3 groups** (each at 2.3 ppm).
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- The **carbonyl group** is at **1700β1750 ppm**, which is typical for a carbonyl group.
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- This configuration is **chemically plausible** for a substituted aromatic ring with **two substituents**, and the **aromatic protons are double-decoupled**, matching the expected pattern for a di-substituted aromatic ring.
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- Aromatic protons at **4.8 (2H, d)** and **4.6 (2H, d)** β **too low** for aromatic protons (typically around 6.5β8.5 ppm), so inconsistent with aromatic structure.
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$$
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```
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**Incorrect** (1 shown)
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- **overall** (0/1 completions correct)
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```text
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<think>
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Okay,
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β
Correct. This matches the definition of an executory contract.
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β
Correct. This is the definition of a wholly executory contract.
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Correct. This is consistent with the definition of a partially executed contract.
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\boxed{
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```
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- **Model repo:** [`cs-552-2026-the-transformers/general_knowledge_model`](https://huggingface.co/cs-552-2026-the-transformers/general_knowledge_model)
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- **Owner(s):** group **the-transformers**
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- **Generated at:** 2026-06-10T21:20:55+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** β 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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|---|---:|---|
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| Math | β | not run |
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| Knowledge | 0.3800 | ok |
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| Multilingual | β | not run |
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| Safety | β | not run |
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**Correct** (1 shown)
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- **reference**: `C`
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- **overall** (1/1 completions correct)
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- **extracted** (β): `C`
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- **completion**:
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```text
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<think>
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Okay, let's see. So the problem is about calculating the probability that an atom decays within a certain time frame, given some previous observations. Let me try to break it down step by step.
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First, the atom has a 32% chance of decaying within 100 minutes. That seems like a decay probability over that period. Since we're dealing with probability, maybe this is a Poisson process? Or mayb
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... [truncated 823 chars] ...
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ore, the answer is C.
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To solve the problem, we model the atomic decay using the exponential distribution, which is well-suited for modeling events that occur randomly over time with a constant rate.
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---
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### Step 1: Use the given probability to find the decay rate
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We are told that the probability of decay within 100 minutes is 32%, or:
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P(\text{decay within 100 min}) = 0.32 = 1 - e^{-\lambda \cdot 100}
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Solving for $\lambda$:
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e^{-100\lambda} = 0.68 \Rightarrow -100\lambda = \ln(0.68) \Rightarrow \lambda = -\frac{\ln(0.68)}{100}
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Using a calculator:
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\lambda \approx \frac{0.3857}{100} = 0.003857 \text{ per minute}
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$$
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### Step 2: Use memoryless property to find the probability of decay in the next 150 minutes
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Since the process is memoryless, the probability of decay between 50 and 200 minutes is the same as the probability of decay between 0 and 150 minutes.
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$$
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P(\text{decay between 50 and 200 min}) = 1 - e^{-\lambda \cdot 150}
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$$
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We already computed $\lambda \cdot 150 = 0.003857 \cdot 150 \approx 0.57855$
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e^{-0.57855} \approx 0.5607
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So the probability of decay is:
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1 - 0.5607 = 0.4393 \approx 44\%
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$$
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---
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### Final Answer:
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</think>
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\boxed{C}
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```
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**Incorrect** (1 shown)
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- **reference**: `A`
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- **overall** (0/1 completions correct)
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- **extracted** (β): `H`
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- **completion**:
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```text
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<think>
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Okay, so I need to find the power spectral density (PSD) of the function f(t), which is defined as the convolution of h(Ο) and Ο(t - Ο). The question gives me the choices, and I need to figure out which one is correct. Let me recall some signal processing concepts here.
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First, the power spectral density. The PSD of a function gives the distribution of power as a function of frequency. For
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... [truncated 823 chars] ...
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Fourier transform of the convolution. Instead, it is derived from the **autocorrelation** of the output.
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---
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### **Step 3: Autocorrelation and Power Spectral Density**
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The power spectral density $ G_f(\omega) $ of a stationary process $ f(t) $ is the **Fourier transform of its autocorrelation function**:
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$$
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G_f(\omega) = \mathcal{F}\{R_{ff}(\tau)\} = \mathcal{F}\{R_{h\phi}(\tau)\},
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$$
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where $ R_{h\phi}(\tau) $ is the convolution of the autocorrelation functions of $ h(t) $ and $ \phi(t) $.
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From the convolution theorem:
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- $ R_{h\phi}(\tau) = R_h(\tau) \cdot R_\phi(\tau) $,
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- Therefore, $ G_f(\omega) = G_h(\omega) \cdot G_\phi(\omega) $,
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- Since $ G_h(\omega) = |H(\omega)|^2 $, the final expression is:
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$$
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G_f(\omega) = |H(\omega)|^2 \cdot G_\phi(\omega).
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$$
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---
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### **Step 4: Matching the Answer**
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Now, we compare this result with the given options. Among them, the only one that matches this form is:
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- **Option H**: $ G_f(\omega) = H(\omega)^2 \cdot G_\phi(\omega) $
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This is **correct**, assuming that the system's frequency response is interpreted as $ H(\omega) $, even though in standard terminology it should be $ |H(\omega)|^2 $.
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---
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### **Final Answer**
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$$
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\boxed{H}
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$$
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```
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