| --- |
| 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 |
| --- |
| |
| <div align="center"> |
| <h1>Palmyra-mini-thinking-a</h1> |
|
|
| </div> |
|
|
| ### 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 |
