QuantFactory/palmyra-mini-thinking-a-GGUF

tags:

• —Coder
• —Math
• —qwen2
• —thinking
• —reasoning model-index:
• —name: Palmyra-mini-thinking-a results: [] license: apache-2.0 language:
• —en


QuantFactory/palmyra-mini-thinking-a-GGUF

This is quantized version of Writer/palmyra-mini-thinking-a created using llama.cpp

Original Model Card

<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 (passrate)' and 0.5481 on 'Olympiadbench (extractivematch)', 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.

Use with transformers

You can run conversational inference using the Transformers Auto classes with the generate() function. Here's an example:

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

vllm serve Writer/palmyra-mini-thinking-a

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 
}

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