Text Generation
PEFT
Safetensors
Transformers
qwen3
grpo
lora
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-thinking-tokens/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cs-552-2026-thinking-tokens/math_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "cs-552-2026-thinking-tokens/math_model") - Transformers
How to use cs-552-2026-thinking-tokens/math_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-thinking-tokens/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-thinking-tokens/math_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-thinking-tokens/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-thinking-tokens/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-thinking-tokens/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-thinking-tokens/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-thinking-tokens/math_model
- SGLang
How to use cs-552-2026-thinking-tokens/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-thinking-tokens/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-thinking-tokens/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-thinking-tokens/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-thinking-tokens/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-thinking-tokens/math_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-thinking-tokens/math_model
Update Automated MNLP evaluation report (2026-06-06)
Browse files- EVAL_REPORT.md +110 -66
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-thinking-tokens/math_model`](https://huggingface.co/cs-552-2026-thinking-tokens/math_model)
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- **Owner(s):** group **thinking-tokens**
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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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## Summary
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| Benchmark | Accuracy | Status |
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| Math | 0.
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| Knowledge | β | not run |
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| Multilingual | β | not run |
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| Safety | β | not run |
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```text
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```text
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```
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- **Model repo:** [`cs-552-2026-thinking-tokens/math_model`](https://huggingface.co/cs-552-2026-thinking-tokens/math_model)
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- **Owner(s):** group **thinking-tokens**
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- **Generated at:** 2026-06-06T17:20:53+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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## Evaluated checkpoint
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- **Commit:** [`49dec59`](https://huggingface.co/cs-552-2026-thinking-tokens/math_model/commit/49dec590cc507292c1b49514d50cffe3835ae4ac)
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- **Message:** Upload folder using huggingface_hub
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- **Committed:** 2026-05-20T12:47:18+00:00
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## Summary
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| Benchmark | Accuracy | Status |
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|---|---:|---|
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| Math | 0.3700 | ok |
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| Knowledge | β | not run |
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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**: `\frac{639}{40}`
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- **overall** (8/8 completions correct)
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- **extracted** (β): `\frac{639}{40}`
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- **completion**:
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```text
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We are given:
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- A map scale: $ 3 $ inches on the map represent $ 10 $ miles in real life.
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- Actual distance between two buildings: $ 53.25 $ miles.
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- We are to find the map distance in inches.
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### Step 1: Determine the scale factor
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We know that:
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$$
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3 \text{ inches} = 10 \text{ miles}
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$$
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We want to find how many inches represent $ 1 $ mile.
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$$
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\text{Scale factor} = \frac{3}{10} \text{ inches per mile}
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$$
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### Step 2: Use the scale factor to find the map distance
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We are given:
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$$
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53.25 \text{ miles} \times \frac{3}{10} \text{ inches/mile} = ?
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\text{Map distance} = 53.25 \times \frac{3}{10}
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\text{Map distance} = \frac{53.25 \times 3}{10}
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\text{Map distance} = \frac{159.75}{10}
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\text{Map distance} = 15.975 \text{ inches}
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$$
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### Step 3: Convert to a fraction
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We now convert $ 15.975 $ into a fraction.
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$$
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15.975 = 15 + 0.975
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0.975 = \frac{975}{1000}
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$$
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\frac{975}{1000} = \frac{39}{40} \quad \text{(simplify by dividing numerator and denominator by 25)}
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So:
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15.975 = 15 + \frac{39}{40} = \frac{600}{40} + \frac{39}{40} = \frac{639}{40}
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$$
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### Final Answer:
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$$
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\boxed{\frac{639}{40}} \text{ inches}
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$$
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```
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**Incorrect** (1 shown)
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- **reference**: `279`
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- **overall** (0/8 completions correct)
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- **extracted** (β): `27`
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- **completion**:
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```text
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We are given a function $ f(n) $ defined as follows:
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- For a positive integer $ n < 1000 $, write $ n $ in base 10 as a string of digits.
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- Let $ b $ be the smallest integer strictly larger than **every digit** in $ n $.
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- Interpret the digit string of $ n $ as a number in base $ b $, and compute its value as $ f(n) $.
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We are to find how many such $ n < 1000 $ satisfy $ f(n) = n $.
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---
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### Ste
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... [truncated 823 chars] ...
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1 \cdot 2^0 = 4 + 2 + 1 = 7 \neq 111 $: β
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Try $ n = 199 $:
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- $ f(199) = 1 \cdot 10^2 + 9 \cdot 10^1 + 9 \cdot 10^0 = 100 + 90 + 9 = 199 $: β
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So $ n = 199 $ works.
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Try $ n = 299 $:
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- $ f(299) = 2 \cdot 100 + 9 \cdot 10 + 9 = 299 $: β
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Try $ n = 399 $:
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- $ f(399) = 3 \cdot 100 + 9 \cdot 10 + 9 = 399 $: β
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Try $ n = 499 $:
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- $ f(499) = 4 \cdot 100 + 9 \cdot 10 + 9 = 499 $: β
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Try $ n = 599 $:
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- $ f(599) = 5 \cdot 100 + 9 \cdot 10 + 9 = 599 $: β
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Try $ n = 699 $:
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- $ f(699) = 6 \cdot 100 + 9 \cdot 10 + 9 = 699 $: β
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Try $ n = 799 $:
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- $ f(799) = 7 \cdot 100 + 9 \cdot 10 + 9 = 799 $: β
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Try $ n = 899 $:
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- $ f(899) = 8 \cdot 100 + 9 \cdot 10 + 9 = 899 $: β
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Try $ n = 999 $:
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- $ f(999) = 9 \cdot 100 + 9 \cdot 10 + 9 = 999 $: β
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So all three-digit numbers where the last two digits are 99 (i.e., 199, 299, ..., 999) satisfy $ f(n) = n $.
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That's 9 numbers.
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---
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### Step 7: Count all valid numbers
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- **Single-digit numbers**: 1 to 9 β 9 numbers
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- **Two-digit numbers**: 19, 29, ..., 99 β 9 numbers
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- **Three-digit numbers**: 199, 299, ..., 999 β 9 numbers
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So total number of such $ n < 1000 $ is:
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$$
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\boxed{9 + 9 + 9 = 27}
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$$
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---
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### β
Final Answer:
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$$
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\boxed{27}
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$$
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```
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