Instructions to use cs-552-2026-vibe-trainers/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-vibe-trainers/math_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-vibe-trainers/math_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-vibe-trainers/math_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-vibe-trainers/math_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-vibe-trainers/math_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-vibe-trainers/math_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-vibe-trainers/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-vibe-trainers/math_model
- SGLang
How to use cs-552-2026-vibe-trainers/math_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-vibe-trainers/math_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-vibe-trainers/math_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-vibe-trainers/math_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-vibe-trainers/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-vibe-trainers/math_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-vibe-trainers/math_model
Update Automated MNLP evaluation report (2026-05-19)
Browse files- EVAL_REPORT.md +88 -43
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-vibe-trainers/math_model`](https://huggingface.co/cs-552-2026-vibe-trainers/math_model)
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- **Owner(s):** group **vibe-trainers**
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- **Generated at:** 2026-05-
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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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|---|---:|---|
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| Math | 0.
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| Knowledge | — | not run |
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| Multilingual | — | not run |
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| Safety | — | not run |
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**Correct** (1 shown)
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- **reference**: `\frac{
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- **overall** (
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- **extracted** (✓): `\frac{
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- **completion**:
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```text
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\[
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\frac{3 \text{ inches}}{10 \text{ miles}} = \frac{x \text{ inches}}{53.25 \text{ miles}}
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\]
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\[
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3 \times 53.25 = 10 \times x
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\]
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Simplifying the left side:
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\[
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159.75 = 10x
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\]
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\[
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x = \frac{159.75}{10} = 15.975
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\]
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\]
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Therefore, the distance between the two buildings on the map is $\boxed{\frac{639}{40}}$ inches.
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```
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**Incorrect** (1 shown)
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- **reference**: `
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- **overall** (0/8 completions correct)
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- **extracted** (✗): `
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- **completion**:
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```text
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This implies:
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$$p = \frac{1}{2}.$$
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Therefore, Derek has a $\boxed{\frac{1}{2}}$ probability of eventually becoming debt-free.
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```
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- **Model repo:** [`cs-552-2026-vibe-trainers/math_model`](https://huggingface.co/cs-552-2026-vibe-trainers/math_model)
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- **Owner(s):** group **vibe-trainers**
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- **Generated at:** 2026-05-19T03:22:52+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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|---|---:|---|
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| Math | 0.3300 | ok |
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| Knowledge | — | not run |
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| Multilingual | — | not run |
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| Safety | — | not run |
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**Correct** (1 shown)
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- **reference**: `\frac{448}{15625}`
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- **overall** (1/8 completions correct)
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- **extracted** (✓): `\frac{448}{15625}`
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- **completion**:
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```text
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To solve this problem, we use the binomial probability formula, which is given by:
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\[ P(X = k) = \binom{n}{k} p^k (1-p)^{n-k} \]
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where:
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- \( n \) is the total number of trials (in this case, 7 islands),
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- \( k \) is the number of successes (in this case, 4 islands with treasure),
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- \( p \) is the probability of success on a single trial (in this case, \(\frac{1}{5}\)).
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1. **Calculate the binomial coefficient**:
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\[ \binom{7}{4} = \frac{7!}{4!(7-4)!} = \frac{7 \times 6 \times 5}{3 \times 2 \times 1} = 35 \]
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2. **Calculate the probability of success raised to the power of 4**:
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\[ \left(\frac{1}{5}\right)^4 = \frac{1}{625} \]
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3. **Calculate the probability of failure raised to the power of 3**:
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\[ \left(1 - \frac{1}{5}\right)^3 = \left(\frac{4}{5}\right)^3 = \frac{64}{125} \]
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4. **Combine these results to find the total probability**:
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\[ P(X = 4) = 35 \times \frac{1}{625} \times \frac{64}{125} = \frac{35 \times 64}{625 \times 125} = \frac{2240}{78125} = \frac{1792}{62500} = \frac{448}{15625} \]
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Conclusion:
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The probability that exactly 4 out of the 7 islands contain treasure is:
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\[ \boxed{\frac{448}{15625}} \]
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```
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**Incorrect** (1 shown)
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- **reference**: `$g(x)=c, g(x)=\lceil x \rceil, g(x)=\lfloor x \rfloor$`
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- **overall** (0/8 completions correct)
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- **extracted** (✗): `h(x) = -x^2`
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- **completion**:
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```text
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1. **Initial Equation Setup:**
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We start with the given functional equation:
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\[
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h(x) = h\left(\frac{h(bx - a) + a}{b}\right)
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\]
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for all \(x \in \mathbb{Q}\), \(a \in \mathbb{Z}\), and \(b \in \mathbb{N}\).
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2. **Assumption of a Solution:**
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Suppose there exists a solution \(h\) such that \(h(q) = 0\) for some \(q \in \mathbb{Q}\).
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3. **Substitution and Simplification:**
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Let \(x
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... [truncated 578 chars] ...
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1) + b}{b}\right)
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\]
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This simplifies to:
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\[
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h\left(\frac{q}{b}\right) = h\left(\frac{h(q - 1) + b}{b}\right)
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\]
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Given \(h(q) = 0\), we have:
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\[
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h\left(\frac{q}{b}\right) = h\left(\frac{h(q - 1) + b}{b}\right) = 0
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\]
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Therefore:
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\[
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h(q - 1) = -b
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\]
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5. **Conclusion for \(h(q)\):**
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By induction, we can generalize that for any integer \(n\):
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\[
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h(q - n) = -bn
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\]
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This implies that for any integer \(n\):
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\[
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h(nq) = -bn^2
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\]
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6. **Final Verification:**
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We need to verify that \(h(x) = -x^2\) satisfies the original equation. Let:
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\[
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h(x) = -x^2
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\]
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Substitute \(h(x) = -x^2\) into the original equation:
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\[
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h(x) = h\left(\frac{h(bx - a) + a}{b}\right)
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\]
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Substituting \(h(x) = -x^2\):
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\[
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-x^2 = h\left(\frac{-\left(bx - a\right)^2 + a}{b}\right)
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\]
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Simplify the argument of \(h\):
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\[
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-x^2 = h\left(\frac{-b^2x^2 + 2abx - a^2 + a}{b}\right)
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\]
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Since \(h\) is defined as \(-y^2\):
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\[
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-x^2 = -\left(\frac{-b^2x^2 + 2abx - a^2 + a}{b}\right)^2
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\]
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This simplifies correctly, confirming that \(h(x) = -x^2\) is indeed a solution.
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Therefore, the solution is:
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\[
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\boxed{h(x) = -x^2}
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\]
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```
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