QuantFactory
/

palmyra-mini-thinking-b-GGUF

+---
+tags:
+- Coder
+- Math
+- qwen2
+- thinking
+- reasoning
+model-index:
+- name: Palmyra-mini-thinking-b
+  results: []
+license: apache-2.0
+language:
+- en
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/palmyra-mini-thinking-b-GGUF
+This is quantized version of [Writer/palmyra-mini-thinking-b](https://huggingface.co/Writer/palmyra-mini-thinking-b) created using llama.cpp
+# Original Model Card
+<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:** Qwen/Qwen2.5-1.5B
+- **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