Model Description
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model: Qwen/Qwen2.5-1.5B
- Context window: 131,072 tokens
- Parameters: 1.7 billion
Model Details
The palmyra-mini-thinking-a model demonstrates exceptional performance in advanced mathematical reasoning and competitive programming. Its capabilities are highlighted by an outstanding score of 0.886 on the 'MATH500' benchmark, showcasing a robust ability to solve complex mathematical problems. The strength of the model in quantitative challenges is further confirmed by its score of 0.8287 on 'gsm8k (strict-match)', which demonstrates proficiency in multi-step arithmetic reasoning. Additionally, the model proves its aptitude for high-level problem-solving with a score of 0.8 on 'AMC23'. The model also shows strong potential in the coding domain, achieving a score of 0.5631 on 'Codeforces (pass_rate)' and 0.5481 on 'Olympiadbench (extractive_match)', indicating competence in generating correct solutions for programming challenges.
Benchmark Performance
This section provides a detailed breakdown of the palmyra-mini-thinking-a model's performance across a standardized set of industry benchmarks. The data is presented in its original order from the source evaluation.
| Benchmark | Score |
|---|---|
| gsm8k (strict-match) | 0.8287 |
| minerva_math(exact_match) | 0.3842 |
| mmlu_pro(exact_match) | 0.2748 |
| hendrycks_math | 0.0054 |
| ifeval (inst_level_loose_acc) | 0.3657 |
| mathqa (acc) | 0.4171 |
| humaneval (pass@1) | 0.2378 |
| BBH (get-answer)(exact_match) | 0.462 |
| mbpp | 0.304 |
| leadboard_musr (acc_norm) | 0.3413 |
| gpqa lighteval gpqa diamond_pass@1:8_samples | 0.3826 |
| AIME24(pass@1)(avg-of-1) | 0.4333 |
| AIME25(pass@1)(avg-of-1) | 0.3667 |
| Livecodebench-codegen (livecodebench/code_generation_lite v4_v5) | 0.1784 |
| AMC23 | 0.8 |
| MATH500 | 0.886 |
| Minerva | 0.3493 |
| Olympiadbench (extractive_match) | 0.5481 |
| Codecontests (pass_rate) | 0.1778 |
| Codeforces (pass_rate) | 0.5631 |
| Taco (pass_rate) | 0.3083 |
| APPS (all_levels) | 0.0447 |
| HMMT23 (extractive_match) | 0.1 |
| Average | 0.380839 |
Use with transformers
You can run conversational inference using the Transformers Auto classes with the generate() function. Here's an example:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Writer/palmyra-mini-thinking-a"
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
vllm serve Writer/palmyra-mini-thinking-a
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Writer/palmyra-mini-thinking-a",
"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.
Citation and Related Information
To cite this model:
@misc{Palmyra-mini-thinking-a,
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