| --- |
| tags: |
| - Coder |
| - Math |
| - qwen2 |
| - thinking |
| - reasoning |
| model-index: |
| - name: Palmyra-mini-thinking-b |
| results: [] |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
|
|
| <div align="center"> |
| <h1>Palmyra-mini-thinking-b</h1> |
|
|
| </div> |
|
|
| <p align="center"> |
| <img src="https://huggingface.co/Writer/palmyra-mini-thinking-b/resolve/main/logo-mini-b%20benchmark-performance.png?download=true" width="800"/> |
| </p> |
|
|
| ### 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 |
