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Model: AiAsistent/Llama-3.1-8B-Instruct-STO-Master
Source: Original Platform

.gitattributes

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README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+language:
+- en
+library_name: transformers
+tags:
+- llama-3.1
+- fine-tuned
+- sto
+- synthetic-data
+- reasoning
+model_creator: AlexH
+model_type: instruct
+pipeline_tag: text-generation
+---
+
+# Llama-3.1-8B-Instruct-STO-Master
+
+## Model Description
+The **Llama-3.1-8B-Instruct-STO-Master** is a high-performance fine-tune of Meta's Llama-3.1-8B-Instruct. This model represents the "Master Version" (Model E) of an extensive research project aimed at pushing the boundaries of 8B parameter architectures.
+
+Unlike traditional Supervised Fine-Tuning (SFT), this model was developed using the **STO (Specialized Task Optimization)** method. This methodology focuses on "Reasoning over Recall," forcing the model to understand the underlying logic of a prompt rather than simply predicting the next most likely token.
+
+### Key Achievements:
+- **Zero-Loss Generalization**: Successfully increased academic and specialized knowledge while maintaining the base model's original "common sense" (Hellaswag) and "ethical alignment" (Moral Scenarios).
+- **Logic Breakthrough**: Achieved a significant increase in the **ARC Challenge** benchmark, surpassing the base model's reasoning capabilities.
+- **Superior IQ**: Internal testing suggests an **IQ increase of 20-30 points** compared to the base Llama 3.1 8B Instruct, particularly in complex problem-solving and multi-step reasoning.
+
+## Training Details
+- **Training Data**: Only **800,000 high-quality tokens**.
+- **Data Source**: 100% Synthetic Data generated via a proprietary high-tier pipeline.
+- **Methodology**: STO (Specialized Task Optimization).
+- **Philosophy**: This model proves that data quality and training methodology (STO) beat raw data quantity. By using just 800k tokens of "Grade 20" synthetic data, we achieved results typically reserved for models with much larger training sets.
+
+For more information on the synthetic data generation used in this project, visit: [LLMResearch - Synthetic Data](https://llmresearch.net/Synthetic-data/)
+
+## Evaluation Results
+
+Evaluation was performed using a sample limit of 250 (due to hardware constraints) across four major benchmarks: **Hellaswag, ARC Challenge, GSM8K, and MMLU**.
+
+### Comparative Performance:
+
+| Benchmark | Meta Llama 3.1 8B Base | **STO-Master (Model E)** | Status |
+| :--- | :--- | :--- | :--- |
+| **MMLU General** | 69.53% | **69.78%** | ✅ Superior |
+| **ARC Challenge** | 52.80% | **53.60%** | 🏆 **Record Logic** |
+| **Hellaswag** | 70.80% | **70.80%** | 🟢 Perfect Recovery |
+| **Moral Scenarios** | 59.60% | **59.20%** | 🟢 Stable Alignment |
+
+### Notable Domain Expertise:
+- **US Foreign Policy**: 90.0%
+- **Government & Politics**: 90.67%
+- **Marketing**: 89.32%
+- **World Religions**: 83.04%
+- **College Biology**: 81.25%
+- **Machine Learning**: 53.57%
+
+## Usage and Testing
+We encourage the community to run their own independent benchmarks on this model. Our internal results show that the model excels in academic writing, professional analysis, and complex STEM tasks.
+
+### Recommendations:
+- **Context Window**: Best results are achieved with a context length of **3096** or higher.
+- **System Prompt**: Works exceptionally well with expert-level personas (e.g., "Senior Researcher," "Professor of Logic").
+
+## Citation & Credits
+
+**Author:** AlexH  
+**Organization:** [LLMResearch.net](https://llmresearch.net)
+
+```bibtex
+@misc{alexh2026llama31sto,
+  author = {AlexH},
+  title = {Llama-3.1-8B-Instruct-STO-Master: Pushing the limits of 8B architectures},
+  year = {2026},
+  publisher = {LLMResearch},
+  organization = {LLMResearch.net},
+  howpublished = {\url{https://huggingface.co/AiAsistent/Llama-3.1-8B-Instruct-STO-Master}}
+}
+
+

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.6"
+}

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