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Model: zannvznn/qwen3-0.6b-math-l45-qlora-merged-fp16
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2026-08-05 22:35:19 +08:00
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*.7z filter=lfs diff=lfs merge=lfs -text
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---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3
- math
- sft
- qlora
- merged-lora
---
# qwen3-0.6b-math-l45-qlora-merged-fp16
This is a merged fp16 model created from `Qwen/Qwen3-0.6B-Base` plus a QLoRA adapter trained on math level 4-5 data.
## Source
- Base model: `Qwen/Qwen3-0.6B-Base`
- Source adapter: `final adapter`
- Merge method: manual LoRA merge, `W_merged = W_base + (B @ A) * scaling`
## Prompt Format
```text
### System:
{system_prompt}
### Problem:
{problem}
### Solution:
```
System prompt:
```text
You are a precise mathematical problem solver.
Follow this exact output contract:
1. Solve the problem step by step with concise reasoning.
2. Use valid LaTeX math notation for mathematical expressions.
3. Preserve LaTeX commands such as \frac{...}{...}, \sqrt{...}, x^{...}, subscripts, equations, and inequalities.
4. Put the final answer on its own last line exactly in this form: Final Answer: \boxed{...}
5. Do not use Markdown code fences.
6. Do not switch to a different answer format.
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
repo_id = "zannvznn/qwen3-0.6b-math-l45-qlora-merged-fp16"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)
```

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\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 messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|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 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.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').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' }}
{{- message.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' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "float16",
"eos_token_id": 151643,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"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
}

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generation_config.json Normal file
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{
"bos_token_id": 151643,
"do_sample": false,
"eos_token_id": 151643,
"max_new_tokens": 2048,
"transformers_version": "5.8.0"
}

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{
"model_id": "Qwen/Qwen3-0.6B-Base",
"run_name": "qwen3-0.6b-math-l45-qlora",
"source_kind": "final adapter",
"source_adapter_dir": "/kaggle/input/datasets/anurhalizah/result/qwen3-0.6b-math-l45-qlora/adapter",
"merged_dir": "/kaggle/working/qwen3-0.6b-math-l45-qlora-merged-fp16",
"merged_matrix_count": 196,
"unique_target_module_count": 196,
"elapsed_seconds": 0.742459690999965,
"lora_alpha": 32.0,
"r": 16,
"use_rslora": false,
"fan_in_fan_out": false
}

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{
"model_id": "Qwen/Qwen3-0.6B-Base",
"prompt_template": "### System:\n{system_prompt}\n\n### Problem:\n{problem}\n\n### Solution:\n",
"system_prompt": "You are a precise mathematical problem solver.\nFollow this exact output contract:\n1. Solve the problem step by step with concise reasoning.\n2. Use valid LaTeX math notation for mathematical expressions.\n3. Preserve LaTeX commands such as \\frac{...}{...}, \\sqrt{...}, x^{...}, subscripts, equations, and inequalities.\n4. Put the final answer on its own last line exactly in this form: Final Answer: \\boxed{...}\n5. Do not use Markdown code fences.\n6. Do not switch to a different answer format.",
"eos_token": "<|endoftext|>",
"padding_side": "right",
"add_final_answer_line_from_last_boxed": true
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"is_local": true,
"local_files_only": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}