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Model: tensorhydra/qwen3-8b-aimo3-tir
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: other
+license_name: qwen-research
+base_model: Qwen/Qwen2.5-8B
+tags:
+- qwen
+- lora
+- merged
+- math
+- reasoning
+- tool-integrated-reasoning
+- aimo
+- safetensors
+datasets:
+- jeannkouagou/aimo3-tool-integrated-reasoning
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-8B AIMO3 Tool-Integrated Reasoning
+
+## Model Summary
+
+A LoRA fine-tuned version of Qwen-8B trained for **tool-integrated reasoning** on the AIMO3 competition dataset (generated by GPT-OSS-120B). The LoRA adapters have been **merged** into the base model and saved in SafeTensors format for straightforward deployment.
+
+| Property | Details |
+|---|---|
+| Base Model | Qwen-8B |
+| Fine-tuning Method | LoRA (merged) |
+| Format | SafeTensors (BF16) |
+| Parameters | ~8B |
+| Disk Size | ~16GB |
+| Max Context | 8192 tokens |
+
+---
+
+## Model Details
+
+### LoRA Configuration
+
+| Hyperparameter | Value |
+|---|---|
+| Rank (r) | 16 |
+| Alpha | 32 |
+| Dropout | 0.05 |
+| Bias | none |
+| Target Modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
+
+### Training Hyperparameters
+
+| Hyperparameter | Value |
+|---|---|
+| Precision | BFloat16 (no quantization) |
+| Epochs | 2 |
+| Steps | 8750 (~1 epoch) |
+| Per-device Batch Size | 2 |
+| Gradient Accumulation Steps | 8 (effective batch: 16) |
+| Learning Rate | 2e-4 |
+| LR Scheduler | Cosine with warmup |
+| Warmup Ratio | 0.03 |
+| Weight Decay | 0.01 |
+| Max Gradient Norm | 1.0 |
+| Max Sequence Length | 8192 |
+| Optimizer | AdamW (Fused) |
+
+### Hardware & Infrastructure
+
+- **Platform**: Kaggle
+- **GPU**: Single NVIDIA H100 (80GB)
+- **Attention**: Flash Attention 2
+- **Optimizations**: Gradient checkpointing, TF32, fused optimizer
+
+---
+
+## Training Data
+
+- **Dataset**: [AIMO3 Tool-Integrated Reasoning Dataset](https://www.kaggle.com/datasets/jeannkouagou/aimo3-tool-integrated-reasoning) (synthesized by GPT-OSS-120B)
+- **Split**: 97.5% train / 2.5% validation
+- **Format**: CSV with problem–solution pairs
+
+**Supported column names:**
+- Input: `problem`, `question`, `input`, `prompt`
+- Output: `solution`, `answer`, `output`, `response`, `completion`
+
+### Instruction Format
+
+Training uses a ChatML-style format:
+```
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+{response}<|im_end|>
+```
+
+### Training Loss
+
+The model is trained for 8750 steps (~ 1 epoch) before stopping. Below are the train and validation loss curves for the entire training session.
+
+![Training Loss Plot](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F32392416%2Ffc9c0a306042ea3b976ed8749150a48d%2Floss_plot.png?generation=1774706655261800&alt=media)
+
+---
+
+## Usage
+
+### Load the Model
+
+Since the LoRA adapters are already merged, PEFT is **not required**:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "tensorhydra/qwen-8b-aimo3-reasoning",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "tensorhydra/qwen-8b-aimo3-reasoning",
+    trust_remote_code=True
+)
+```
+
+### Inference
+```python
+prompt = "Solve this problem: What is 2 + 2?"
+
+formatted_prompt = f"user\n{prompt}\nassistant\n"
+
+inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=False)
+print(response)
+```
+
+### Batch Inference
+```python
+prompts = [
+    "Solve: 15 + 27 = ?",
+    "What is the derivative of x^2?",
+    "Calculate the area of a circle with radius 5"
+]
+
+formatted_prompts = [
+    f"user\n{p}\nassistant\n"
+    for p in prompts
+]
+
+inputs = tokenizer(formatted_prompts, return_tensors="pt", padding=True).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+
+for response in tokenizer.batch_decode(outputs, skip_special_tokens=False):
+    print(response)
+    print("-" * 80)
+```
+
+### Quantized Inference (Lower VRAM)
+```python
+# 8-bit (~8GB VRAM)
+model = AutoModelForCausalLM.from_pretrained(
+    "tensorhydra/qwen-8b-aimo3-reasoning",
+    load_in_8bit=True,
+    device_map="auto"
+)
+
+# 4-bit (~4GB VRAM)
+model = AutoModelForCausalLM.from_pretrained(
+    "tensorhydra/qwen-8b-aimo3-reasoning",
+    load_in_4bit=True,
+    device_map="auto"
+)
+```
+
+---
+
+## Memory Requirements
+
+| Mode | VRAM |
+|---|---|
+| BF16 (full) | ~16GB |
+| 8-bit quantized | ~8GB |
+| 4-bit quantized | ~4GB |
+
+---
+
+## Repository Structure
+```
+model/
+├── config.json
+├── generation_config.json
+├── model.safetensors.index.json
+├── model-00001-of-0000X.safetensors
+├── ...
+├── tokenizer_config.json
+├── tokenizer.json
+└── special_tokens_map.json
+```
+
+---
+
+## Intended Use
+
+- Mathematical reasoning and problem solving  
+- Tool-integrated step-by-step reasoning  
+- Educational and research applications  
+- Production deployment (merged model, no PEFT dependency)
+
+## Limitations
+
+- Fine-tuned on a narrow reasoning domain; may not generalize well to other tasks
+- Hard context limit of 8192 tokens
+- Performance is bounded by the quality and distribution of the synthetic training data
+- Full merged model requires ~16GB storage (vs. ~100–200MB for LoRA adapters alone)
+
+---
+
+## Links
+
+- **Dataset**: [jeannkouagou/aimo3-tool-integrated-reasoning](https://www.kaggle.com/datasets/jeannkouagou/aimo3-tool-integrated-reasoning)
+- **Fine-tuning Notebook**: [tensorhydra/qwen3-8b-aimo3-finetune](https://www.kaggle.com/code/tensorhydra/qwen3-8b-aimo3-finetune)
+
+---
+
+## Citation
+```bibtex
+@misc{qwen-lora-aimo3,
+  title   = {Qwen-8B LoRA Fine-tuned for Tool-Integrated Reasoning},
+  author  = {tensorhydra},
+  year    = {2025},
+  howpublished = {Kaggle Model Hub},
+  note    = {Merged LoRA model in SafeTensors format}
+}
+```
+
+---
+
+## Acknowledgements
+
+- **Base model**: [Qwen-8B](https://huggingface.co/Qwen) by Alibaba Cloud
+- **Training frameworks**: Hugging Face Transformers & PEFT
+- **Dataset synthesis**: GPT-OSS-120B
+- **Serialization**: SafeTensors
+- **Training platform**: Kaggle (H100 GPU)
+
+## License
+
+This model inherits the license of the base Qwen-8B model. Please refer to the [Qwen license terms](https://huggingface.co/Qwen/Qwen2.5-8B/blob/main/LICENSE) before use.

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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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 %}

config.json

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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": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "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",
+    "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "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": "4.57.1"
+}

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