---
tags:
- Coder
- Math
- qwen2
- thinking
- reasoning
model-index:
- name: Palmyra-mini-thinking-b
results: []
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
Palmyra-mini-thinking-b
### Model Description
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** nvidia/OpenReasoning-Nemotron-1.5B
- **Context window:** 131,072 tokens
- **Parameters:** 1.7 billion
## Introduction
Palmyra-mini-thinking-b represents a significant step forward in generative AI, demonstrating exceptional capabilities in complex reasoning and problem-solving domains. This model excels in mathematical and programming challenges, showcasing a robust understanding of abstract concepts and logical structures. Its performance is not just a measure of its power but a testament to its specialized training, which has honed its ability to tackle tasks that demand deep, multi-step thinking.
## Mathematical Prowess
The model's mathematical abilities are particularly noteworthy. It achieves an impressive score of 0.925 on the AMC23 benchmark, indicating a strong grasp of advanced high school mathematics. This is further complemented by its performance on MATH500, where it scores 0.882, proving its proficiency across a wide range of mathematical problems. The model also shows its strength in competitive mathematics, scoring 0.6 on AIME24(pass@1)(avg-of-1) and 0.5733 on Olympiadbench (extractive_match). These scores highlight the model's capacity for sophisticated mathematical reasoning, making it a powerful tool for both educational and research applications.
## Excellence in Competitive Programming
Beyond mathematics, Palmyra-mini-thinking-b demonstrates strong performance in the competitive programming arena. Its score of 0.6343 on the Codeforces (pass_rate) benchmark underscores its ability to understand complex algorithmic problems and generate correct, efficient code. This capability suggests the model is well-suited for tasks involving code generation, debugging, and algorithmic design, making it a valuable asset for software developers and computer science researchers.
## Benchmark Scores (sampling params: temperature:0.6, top_p:0.95)
Pass@1(avg-of-64)
| Benchmark | Pass@1 (avg-of-64) | Majority@64 |
| :-------- | :------------------- | :----------- |
| AIME24 | 59.43% | 71.67% |
| AIME25 | 49.69% | 60.00% |
| GPQA | 42.01% | 47.22% |
| HMMT25 | 27.86% | 30.00% |
| HLE | 5.22% | N/A |
| MMLU-PRO | 55.49% | 60.60% |
| MATH500 | 93.80% | 95.40% |
| LCB | 34.51% | N/A |
LCB here is version v6_2408_2505
Pass@1(avg-of-1)
| Benchmark | Score (%) |
|:-----------------------------------------------------------------|------------:|
| GSM8K (strict-match) | 42.68% |
| Minerva Math (exact match) | 7.08% |
| MMLU-PRO (exact match) | 29.26% |
| MATH (Hendrycks) | 0.16% |
| IFEval (inst_level_loose_acc) | 32.97% |
| MathQA (acc) | 30.45% |
| HumanEval (pass@1) | 7.32% |
| BBH (get-answer)(exact match) | 28.80% |
| MBPP | 16.80% |
| GPQA (diamond, pass@1: 8 samples) | 39.58% |
| AIME24 (pass@1)(avg-of-1) | 60.00% |
| AIME25 (pass@1)(avg-of-1) | 50.00% |
| Livecodebench-codegen (livecodebench/code_generation_lite v4_v5) | 28.73% |
| AMC23 | 92.50% |
| MATH500 | 88.20% |
| Minerva | 29.41% |
| Olympiadbench (extractive_match) | 57.33% |
| Codecontests (pass_rate) | 20.18% |
| Codeforces (pass_rate) | 63.43% |
| Taco (pass_rate) | 34.56% |
| APPS (all_levels) | 5.84% |
| HMMT (Feb 2025) (extractive_match) | 23.33% |
| Average | 35.94% |
### Use with transformers
You can run conversational inference using the Transformers Auto classes with the `generate()` function. Here's an example:
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Writer/palmyra-mini-thinking-b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="flash_attention_2",
)
messages = [
{
"role": "user",
"content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
}
],
input_ids = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)
gen_conf = {
"max_new_tokens": 256,
"eos_token_id": tokenizer.eos_token_id,
"temperature": 0.3,
"top_p": 0.9,
}
with torch.inference_mode():
output_id = model.generate(input_ids, **gen_conf)
output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])
print(output_text)
```
## Running with vLLM
```py
vllm serve Writer/palmyra-mini-thinking-b
```
```py
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Writer/palmyra-mini-thinking-b",
"messages": [
{
"role": "user",
"content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
}
],
"max_tokens": 8000,
"temperature": 0.2
}'
```
## Ethical Considerations
As with any language model, there is a potential for generating biased or inaccurate information. Users should be aware of these limitations and use the model responsibly.
### Footnotes
- Base model: This model builds on NVIDIA's OpenReasoning-Nemotron-1.5B (`https://huggingface.co/nvidia/OpenReasoning-Nemotron-1.5B`).
- Evaluation methodology:
- Pass@1 (avg-of-1): computed using `lm_eval` and `lighteval`.
- Pass@1 (avg-of-64) and Majority@64: computed using `nemoskills`.
### Citation and Related Information
To cite this model:
```
@misc{Palmyra-mini-thinking-b,
author = {Writer Engineering team},
title = {{Palmyra-mini: A powerful LLM designed for math and coding}},
howpublished = {\url{https://dev.writer.com}},
year = 2025,
month = Sep
}
```
Contact Hello@writer.com