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Model: khazarai/BioGenesis-ToT
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- sft
+- reasoning
+- unsloth
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- unsloth/Qwen3-1.7B
+pipeline_tag: text-generation
+---
+
+# Model Card for BioGenesis-ToT
+
+
+![alt="General Benchmark Comparison Chart"](benchmark/BioGenesis-ToT.png)
+
+- **Overall Success Rate**:
+  - khazarai/BioGenesis-ToT: **51.45**
+  - Qwen/Qwen3-1.7B: **46.82**
+ 
+- **Benchmark**: [emre/TARA_Turkish_LLM_Benchmark](https://huggingface.co/datasets/emre/TARA_Turkish_LLM_Benchmark)
+
+
+BioGenesis-ToT is a fine-tuned version of Qwen3-1.7B, optimized for mechanistic reasoning and explanatory understanding in biology.
+This model has been trained on the [moremilk/ToT-Biology](https://huggingface.co/datasets/moremilk/ToT-Biology) dataset — a reasoning-rich collection of biology questions emphasizing why and how processes occur, rather than simply what happens.
+ 
+The model demonstrates strong capabilities in:
+- Structured biological explanation generation
+- Logical and causal reasoning
+- Chain-of-thought (ToT) reasoning in scientific contexts
+- Interdisciplinary biological analysis (e.g., bioengineering, medicine, ecology)
+
+## Uses
+
+### 🚀 Intended Use
+
+- Educational and scientific explanation generation
+- Biological reasoning and tutoring applications
+- Model interpretability research
+- Training datasets for reasoning-focused LLMs
+
+
+### ⚠️ Limitations
+
+- Not a replacement for expert biological judgment
+- May occasionally over-generalize or simplify complex phenomena
+- Limited to reasoning quality within biological contexts (not trained for creative writing or coding)
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("khazarai/BioGenesis-ToT")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/BioGenesis-ToT",
+    device_map={"": 0}
+)
+
+
+question = """
+Describe the composition of the plasma membrane and explain how its structure relates to its function of selective permeability.
+"""
+
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 2200,
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+
+## 🧪 Dataset: moremilk/ToT-Biology
+
+The ToT-Biology dataset emphasizes mechanistic understanding and explanatory reasoning within biology.
+It’s designed to help AI models develop interpretable, step-by-step reasoning abilities for complex biological systems.
+
+It spans a wide range of biological subdomains:
+- Foundational biology: Cell biology, genetics, evolution, and ecology
+- Advanced topics: Systems biology, synthetic biology, computational biophysics
+- Applied domains: Medicine, agriculture, bioengineering, and environmental science
+
+Dataset features include:
+
+- 🧩 Logical reasoning styles — deductive, inductive, abductive, causal, and analogical
+- 🧠 Problem-solving techniques — decomposition, elimination, systems thinking, trade-off analysis
+- 🔬 Real-world problem contexts — experiment design, pathway mapping, and data interpretation
+- 🌍 Practical relevance — bridging theoretical reasoning and applied biological insight
+- 🎓 Educational focus — for both AI training and human learning in scientific reasoning
+
+
+## 🧭 Objective
+
+This fine-tuning project aims to build an interpretable reasoning model capable of:
+
+- Explaining biological mechanisms clearly and coherently
+- Demonstrating transparent, step-by-step thought processes
+- Applying logical reasoning techniques to biological and interdisciplinary problems
+- Supporting educational and research use cases where reasoning transparency matters
+
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@model{khazarai/BioGenesis-ToT,
+  title     = {BioGenesis-ToT: A Fine-Tuned Model for Explanatory Biological Reasoning},
+  author    = {Rustam Shiriyev},
+  year      = {2025},
+  publisher = {Hugging Face},
+  base_model = {Qwen3-1.7B},
+  dataset   = {moremilk/ToT-Biology},
+  license   = {MIT}
+}
+```

added_tokens.json

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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 forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- 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>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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"
+  ],
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+  "head_dim": 128,
+  "hidden_act": "silu",
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+  "intermediate_size": 6144,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
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+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
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+    "rope_type": "default"
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+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.0.0"
+}

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