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Model: Writer/palmyra-mini-thinking-a
Source: Original Platform

.gitattributes

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README.md

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+---
+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

chat_template.jinja

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+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151646,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention"
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+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 28,
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+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

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special_tokens_map.json

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