--- tags: - Coder - Math - qwen2 - thinking - reasoning model-index: - name: Palmyra-mini-thinking-a results: [] license: apache-2.0 pipeline_tag: text-generation language: - en library_name: transformers ---

Palmyra-mini-thinking-a

### 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: ```py 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 ```py vllm serve Writer/palmyra-mini-thinking-a ``` ```py 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