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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-b |
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results: [] |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/palmyra-mini-thinking-b-GGUF |
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This is quantized version of [Writer/palmyra-mini-thinking-b](https://huggingface.co/Writer/palmyra-mini-thinking-b) created using llama.cpp |
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# Original Model Card |
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<div align="center"> |
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<h1>Palmyra-mini-thinking-b</h1> |
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</div> |
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<p align="center"> |
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<img src="https://huggingface.co/Writer/palmyra-mini-thinking-b/resolve/main/logo-mini-b%20benchmark-performance.png?download=true" width="800"/> |
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</p> |
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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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## Introduction |
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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. |
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## Mathematical Prowess |
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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. |
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## Excellence in Competitive Programming |
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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. |
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## Benchmark Scores (sampling params: temperature:0.6, top_p:0.95) |
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Pass@1(avg-of-64) |
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| Benchmark | Pass@1 (avg-of-64) | Majority@64 | |
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| :-------- | :------------------- | :----------- | |
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| AIME24 | 59.43% | 71.67% | |
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| AIME25 | 49.69% | 60.00% | |
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| GPQA | 42.01% | 47.22% | |
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| HMMT25 | 27.86% | 30.00% | |
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| HLE | 5.22% | N/A | |
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| MMLU-PRO | 55.49% | 60.60% | |
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| MATH500 | 93.80% | 95.40% | |
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| LCB | 34.51% | N/A | |
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LCB here is version v6_2408_2505 |
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Pass@1(avg-of-1) |
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| Benchmark | Score (%) | |
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|:-----------------------------------------------------------------|------------:| |
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| GSM8K (strict-match) | 42.68% | |
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| Minerva Math (exact match) | 7.08% | |
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| MMLU-PRO (exact match) | 29.26% | |
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| MATH (Hendrycks) | 0.16% | |
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| IFEval (inst_level_loose_acc) | 32.97% | |
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| MathQA (acc) | 30.45% | |
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| HumanEval (pass@1) | 7.32% | |
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| BBH (get-answer)(exact match) | 28.80% | |
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| MBPP | 16.80% | |
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| GPQA (diamond, pass@1: 8 samples) | 39.58% | |
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| AIME24 (pass@1)(avg-of-1) | 60.00% | |
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| AIME25 (pass@1)(avg-of-1) | 50.00% | |
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| Livecodebench-codegen (livecodebench/code_generation_lite v4_v5) | 28.73% | |
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| AMC23 | 92.50% | |
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| MATH500 | 88.20% | |
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| Minerva | 29.41% | |
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| Olympiadbench (extractive_match) | 57.33% | |
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| Codecontests (pass_rate) | 20.18% | |
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| Codeforces (pass_rate) | 63.43% | |
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| Taco (pass_rate) | 34.56% | |
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| APPS (all_levels) | 5.84% | |
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| HMMT (Feb 2025) (extractive_match) | 23.33% | |
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| Average | 35.94% | |
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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-b" |
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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-b |
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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-b", |
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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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### Footnotes |
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- Base model: This model builds on NVIDIA's OpenReasoning-Nemotron-1.5B (`https://huggingface.co/nvidia/OpenReasoning-Nemotron-1.5B`). |
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- Evaluation methodology: |
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- Pass@1 (avg-of-1): computed using `lm_eval` and `lighteval`. |
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- Pass@1 (avg-of-64) and Majority@64: computed using `nemoskills`. |
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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-b, |
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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 |
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