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--- |
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tags: |
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- Coder |
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- Math |
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- qwen2 |
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- thinking |
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- reasoning |
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model-index: |
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- name: Palmyra-mini-thinking-a |
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results: [] |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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language: |
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- en |
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library_name: transformers |
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--- |
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<div align="center"> |
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<h1>Palmyra-mini-thinking-a</h1> |
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</div> |
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### Model Description |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** Qwen/Qwen2.5-1.5B |
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- **Context window:** 131,072 tokens |
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- **Parameters:** 1.7 billion |
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## Model Details |
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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. |
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## Benchmark Performance |
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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. |
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| Benchmark | Score | |
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|:-----------------------------------------------------------------|---------:| |
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| gsm8k (strict-match) | 0.8287 | |
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| minerva_math(exact_match) | 0.3842 | |
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| mmlu_pro(exact_match) | 0.2748 | |
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| hendrycks_math | 0.0054 | |
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| ifeval (inst_level_loose_acc) | 0.3657 | |
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| mathqa (acc) | 0.4171 | |
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| humaneval (pass@1) | 0.2378 | |
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| BBH (get-answer)(exact_match) | 0.462 | |
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| mbpp | 0.304 | |
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| leadboard_musr (acc_norm) | 0.3413 | |
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| gpqa lighteval gpqa diamond_pass@1:8_samples | 0.3826 | |
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| AIME24(pass@1)(avg-of-1) | 0.4333 | |
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| AIME25(pass@1)(avg-of-1) | 0.3667 | |
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| Livecodebench-codegen (livecodebench/code_generation_lite v4_v5) | 0.1784 | |
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| AMC23 | 0.8 | |
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| MATH500 | 0.886 | |
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| Minerva | 0.3493 | |
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| Olympiadbench (extractive_match) | 0.5481 | |
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| Codecontests (pass_rate) | 0.1778 | |
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| Codeforces (pass_rate) | 0.5631 | |
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| Taco (pass_rate) | 0.3083 | |
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| APPS (all_levels) | 0.0447 | |
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| HMMT23 (extractive_match) | 0.1 | |
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| Average | 0.380839 | |
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### Use with transformers |
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You can run conversational inference using the Transformers Auto classes with the `generate()` function. Here's an example: |
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```py |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "Writer/palmyra-mini-thinking-a" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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attn_implementation="flash_attention_2", |
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) |
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messages = [ |
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{ |
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"role": "user", |
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"content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?" |
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} |
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], |
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input_ids = tokenizer.apply_chat_template( |
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messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" |
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) |
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gen_conf = { |
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"max_new_tokens": 256, |
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"eos_token_id": tokenizer.eos_token_id, |
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"temperature": 0.3, |
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"top_p": 0.9, |
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} |
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with torch.inference_mode(): |
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output_id = model.generate(input_ids, **gen_conf) |
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output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :]) |
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print(output_text) |
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``` |
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## Running with vLLM |
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```py |
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vllm serve Writer/palmyra-mini-thinking-a |
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``` |
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```py |
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curl -X POST http://localhost:8000/v1/chat/completions \ |
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-H "Content-Type: application/json" \ |
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-d '{ |
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"model": "Writer/palmyra-mini-thinking-a", |
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"messages": [ |
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{ |
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"role": "user", |
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"content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?" |
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} |
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], |
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"max_tokens": 8000, |
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"temperature": 0.2 |
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}' |
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``` |
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## Ethical Considerations |
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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. |
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### Citation and Related Information |
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To cite this model: |
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``` |
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@misc{Palmyra-mini-thinking-a, |
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author = {Writer Engineering team}, |
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title = {{Palmyra-mini: A powerful LLM designed for math and coding}}, |
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howpublished = {\url{https://dev.writer.com}}, |
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year = 2025, |
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month = Sep |
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} |
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``` |
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Contact Hello@writer.com |
