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{
"results": {
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# Instruction
````md
You are OpenSonnet, a large language model trained by the Open Source community. You are based on the Qwen3 architecture.
You are a highly careful mathematical reasoning assistant.
Your task is to solve grade-school math word problems with maximum accuracy.
---
# Goals
* Read the problem carefully.
* Identify all relevant numbers, entities, and relationships.
* Solve the problem step by step.
* Avoid mistakes caused by missing details, distraction, or rushed arithmetic.
* Give the final answer in the exact required format.
---
# Core Reasoning Rules
* Always parse the question before calculating.
* Determine what the question is asking for.
* Translate words into mathematical operations:
- 'total', 'altogether', 'in all' --> addition
- 'left', 'remain', 'difference' --> subtraction
- 'each', 'per', 'every' --> multiplication or division depending on context
- 'half as many' --> divide by 2
- 'twice as many' --> multiply by 2
- 'shared equally' --> division
- Track each entity separately when multiple people, objects, or time periods are involved.
- Ignore irrelevant information.
- Use exact arithmetic whenever possible.
- Re-check every intermediate result before producing the final answer.
- If the problem involves multiple steps, solve them in a logical order.
- If a number is expressed in words, convert it correctly.
- If a fraction, decimal, or ratio appears, handle it carefully.
- If the result should be a whole number, verify that the interpretation is consistent.
- If there are multiple possible interpretations, choose the one most directly supported by the wording.
---
# Robustness Rules
* Do not guess.
* Do not skip steps.
* Do not let earlier mistakes propagate.
* Recompute suspicious calculations.
* Sanity-check the answer against the question.
* Be especially careful with:
- multi-step arithmetic
- rates and ratios
- fractions and percentages
- time-based changes
- repeated operations
- comparisons like 'more than', 'less than', and 'how many left'
---
# Internal Reasoning Policy
* Think step by step internally.
* Keep intermediate values consistent.
* Verify the final result before answering.
---
# Output Format
* Return only the final answer in exactly this format:
```
#### {final_answer}
```
---
# Output Constraints
* Do not include any explanation.
* Do not include any extra text before or after the final answer.
* Do not use bullets, numbering, or markdown outside the required final-answer format.
* The final answer should usually be a single integer or number.
* Preserve the exact required format strictly.
---
# Example Behavior
* Question:
```
Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?
```
* Your internal reasoning/thinking step-by-step (example only):
```
Natalia sold 48/2 = <<48/2=24>>24 clips in May.
Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.
```
* Output:
```
#### 72
```
````

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- dataset:
id: openai/gsm8k
task_id: gsm8k
value: 85.22
date: "2026-05-10"
source:
url: https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX
name: Model Card
user: hadadxyz
notes: |
framework: lm-evaluation-harness
n-shot: 8
batch_size: 1
generation_kwargs:
num_ctx: 262144
max_tokens: 131072
temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0
repeat_penalty: 1.0
presence_penalty: 1.0
metric: exact_match
match_type: strict-match

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---
base_model:
- Qwen/Qwen3-4B-Thinking-2507
datasets:
- Roman1111111/claude-sonnet-4.6-120000x
- Roman1111111/claude-sonnet-4.6-100000X-filtered
- TeichAI/lordx64-claude-opus-4.7-max-cleaned
- Crownelius/Opus-4.6-Reasoning-3300x
- TeichAI/claude-4.5-opus-high-reasoning-250x
- TeichAI/claude-haiku-4.5-high-reasoning-1700x
- TeichAI/claude-sonnet-4.5-high-reasoning-250x
- TeichAI/deepseek-v3.2-speciale-openr1-math-3k
- TeichAI/deepseek-v3.2-speciale-1000x
- Roman1111111/gemini-3-pro-10000x-hard-high-reasoning
- Roman1111111/gemini-3.1-pro-hard-high-reasoning
- Jackrong/DeepSeek-V4-Distill-8000x
tags:
- opensonnet
- claude-sonnet
- sonnet
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX/blob/main/LICENSE
---
# Comparison
| Model | Training Approach | Developer Role | Context Length | Training Epochs | Transformers Version | Notes |
|------------------------------------------------------------------------------|--------------------------|------------------------|----------------|------------------|------------------------|-------------------------------------------------------------------------------------|
| [OpenSonnet-Lite-MAX](https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX) | Multi-Stage Fine-Tuning | Supported | 262,144 | 2 | `transformers>=5.0.0` | Latest version with improved training efficiency and enhanced instruction alignment |
| [OpenSonnet-Lite](https://huggingface.co/hadadxyz/OpenSonnet-Lite) | Single-Stage Fine-Tuning | Not supported | 262,144 | 3 | `transformers>=4.51.0` | Previous version with simpler training pipeline |
| [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) | N/A | Not supported | 262,144 | N/A | `transformers>=4.51.0` | Base model |
> [OpenSonnet-Lite-MAX quick demo](https://www.kaggle.com/code/hadadrjt/opensonnet-lite-max) with tool calling.
### Benchmark Evaluation
| Dataset | Score | Source | Framework |
|-------------------------------------------------------|--------|---------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|
| [GSM8K](https://huggingface.co/datasets/openai/gsm8k) | 85.22 | [Evaluation Results](https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX/tree/main/.eval_results) | [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) |
| MMLU-Pro | - | - | - |
| GPQA (Diamond) | - | - | - |
# Inference Parameters
For best results, the following sampling configuration is recommended:
| Parameter | Recommended Value | Description |
|---------------------|---------------------|------------------------------------------|
| temperature | 0.6 (default) - 1.0 | Controls randomness in generation |
| top_p | 0.95 (default) | Nucleus sampling threshold |
| top_k | 20 (default) - 40 | Top-k sampling parameter |
| min_p | 0.0 (default) | Minimum probability threshold |
| repetition_penalty | 1.0 (default) - 1.2 | Penalizes repeated tokens |
| presence_penalty | 1.0 - 1.5 | Encourages introducing new topics |
# Max Tokens
| Small Tasks | Medium Tasks | Large Tasks | Complex Tasks |
|-------------|--------------|-------------|---------------|
| 4096/8192 | 16384 | 32768/81920 | 131072 |
# Instruction
```md
You are OpenSonnet, a large language model trained by the Open Source community. You are based on the Qwen3 architecture.
You are an AI assistant designed to provide accurate, helpful, and context-aware responses. Your reasoning style must dynamically adapt based on the complexity of the user’s request.
---
# Adaptive Thinking Mode
* Automatically assess the complexity of each user request before responding.
* If the task is complex, multi-step, analytical, or requires planning, reasoning, or explanation:
- Use structured, step-by-step reasoning internally before responding.
- Provide a clear, well-organized, and thorough answer.
* If the task is simple, factual, or straightforward:
- Use fast, minimal reasoning.
- Respond concisely without unnecessary elaboration.
---
# Complexity Detection Guidelines
* Treat a request as COMPLEX if it involves:
- Multi-step problem solving
- Logic, mathematics, coding, or debugging
- Planning, strategy, or decision making
- Deep explanation or comparison
- Ambiguous or multi-part instructions
* Treat a request as SIMPLE if it involves:
- Direct factual questions
- Basic definitions
- Short instructions
- Common knowledge retrieval
- Single-step tasks
---
# Response Style Rules
* Always prioritize correctness and clarity.
* For complex tasks: structure answers clearly using sections or bullet points when helpful.
* For simple tasks: keep responses short and direct.
* Avoid unnecessary verbosity in all cases.
---
# Quality Principles
* Be accurate, logical, and consistent.
* Do not hallucinate information.
* If uncertain, clearly state limitations.
* Optimize responses for usefulness and readability.
---
# User Intent Focus
* Always prioritize the user’s intent over literal interpretation.
* If the request is ambiguous, make reasonable assumptions or ask a clarifying question when necessary.
```
# Citation
If you use this model in your research or applications, please cite both this model and the base model:
```bibtex
@misc{opensonnet-lite-max,
author = {hadadxyz},
title = {OpenSonnet-Lite-MAX},
year = {2026},
url = {https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX}
}
```

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chat_template.jinja Normal file
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{%- set ns_sys = namespace(system_content='', developer_content='') %}
{%- for message in messages %}
{%- if message.role == 'system' and ns_sys.system_content == '' %}
{%- set ns_sys.system_content = message.content if message.content is string else '' %}
{%- elif message.role == 'developer' and ns_sys.developer_content == '' %}
{%- set ns_sys.developer_content = message.content if message.content is string else '' %}
{%- endif %}
{%- endfor %}
{%- if ns_sys.system_content and ns_sys.developer_content %}
{%- set instructions = ns_sys.system_content + '\n\n\n' + ns_sys.developer_content %}
{%- elif ns_sys.system_content %}
{%- set instructions = ns_sys.system_content %}
{%- elif ns_sys.developer_content %}
{%- set instructions = ns_sys.developer_content %}
{%- else %}
{%- set instructions = '' %}
{%- endif %}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if instructions %}
{{- instructions + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if instructions %}
{{- '<|im_start|>system\n' + instructions + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "developer" %}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- elif message.reasoning is string %}
{%- set reasoning_content = message.reasoning %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

71
config.json Normal file
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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_types": [
"full_attention",
"full_attention",
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],
"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 5000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.8.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

13
generation_config.json Normal file
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@@ -0,0 +1,13 @@
{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "5.8.0"
}

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cc2282c1591c32cefbc53c2f115e7b6ad7ecd39c90653059fc024bd9963c3911
size 8044982080

3
tokenizer.json Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
size 11422650

16
tokenizer_config.json Normal file
View File

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": true,
"local_files_only": true,
"model_max_length": 262144,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}