Upload intellix-oQ8 via oMLX

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,215 @@
+---
+library_name: peft
+pipeline_tag: text-generation
+base_model: mediusware/intellix-foundation
+model_type: intellix
+tags:
+- business-ai
+- mediusware
+- proprietary
+- sft
+- transformers
+- lora
+- gguf
+- business-intelligence
+- office-automation
+- security-focused
+---
+
+<p align="center">
+  <img src="https://huggingface.co/mediusware-ai/intellix/resolve/main/logo.png" width="800">
+</p>
+
+# Model Card: mw-intellix
+
+**mw-intellix** is a high-capacity, fine-tuned large language model (LLM) designed specifically for enterprise-grade applications. It addresses the critical need for secure, accurate, and professional AI in the business world (B2B). Developed by **[Mediusware](https://mediusware.com)**, it offers a state-of-the-art solution that prioritizes data privacy and professional reasoning.
+
+---
+
+## 1. Model Details
+
+- **Model Developer:** Mediusware
+- **Model Date:** March 2026
+- **Model Version:** 1.0.0
+- **Model Type:** Causal Language Model (Fine-tuned via PEFT/LoRA and GGUF quantized)
+- **Base Model:** Proprietary Business-Oriented Foundation (Optimized Qwen architecture)
+- **License:** Proprietary (Mediusware)
+
+## 2. Intended Use
+
+### Primary Intended Uses
+- **Enterprise Communication:** Drafting professional emails, client updates, and internal memos.
+- **Policy & Security Auditing:** Generating and reviewing business security policies and compliance documentation.
+- **Knowledge Synthesis:** Summarizing complex business documents into executive highlights.
+- **Decision Support:** Providing reasoned insights for project management and business logic.
+
+### Primary Intended Users
+- Business professionals and executives.
+- IT security and compliance officers.
+- Enterprise software developers integrating AI into professional workflows.
+
+### Out-of-Scope Use Cases
+- Non-professional or casual conversational use.
+- High-stakes medical, legal, or financial advice without human oversight.
+- Generation of fictional or creative content not grounded in business reality.
+
+## 3. Factors
+
+### Relevant Factors
+- **Professional Tone:** The model is evaluated based on its ability to maintain a consistent, corporate-ready voice.
+- **Security Compliance:** Evaluation focuses on the model's adherence to security protocols and data privacy constraints.
+- **Accuracy:** Minimization of hallucinations in professional contexts (e.g., policy drafting).
+
+### Evaluation
+Evaluations were conducted using a proprietary enterprise benchmark suite and real-world business scenarios to ensure the model's readiness for B2B deployment.
+
+## 4. Metrics
+
+### Model Performance Measures
+- **Throughput:** Measured in tokens per second (TPS) for real-time responsiveness.
+- **Latency:** Time-to-first-token (TTFT) and total response time.
+- **Persona Adherence:** Qualitative and quantitative scoring of professional tone consistency.
+
+## 5. Evaluation Results
+
+### Quantitative Performance (March 2026)
+*Tested on Q8_0 GGUF via optimized local inference.*
+
+| Metric | Performance Value |
+| :--- | :--- |
+| **Average Throughput** | **196.08 tokens/sec** |
+| **Average Latency** | **0.68 seconds** |
+| **Peak Throughput** | **199.48 tokens/sec** |
+| **Model Footprint** | **2.0 GB** |
+
+## 6. Training Data
+
+### Data Sources
+The model was fine-tuned on a massive, curated dataset including:
+- Professional business correspondence and templates.
+- Industry-standard security policies and compliance manuals.
+- Technical documentation for enterprise software.
+- High-quality project management logs and reports.
+
+### Data Preprocessing
+Data was rigorously cleaned to remove PII (Personally Identifiable Information) and informal/low-quality text, ensuring the model's output remains strictly professional.
+
+## 7. Quantitative Analysis
+
+### Benchmark Scenarios
+The following scenarios were used to validate the model's business intelligence:
+1. **Scenario A:** Draft a secure data handling policy for a fintech startup.
+2. **Scenario B:** Summarize a 50-page internal audit report into 5 key action items.
+3. **Scenario C:** Write a professional apology to a high-value client for a project delay.
+
+## 8. Fine-Tuning Process
+
+### Methodology
+**mw-intellix** was fine-tuned using the **Unsloth** library for memory-efficient and fast training. The process utilized **LoRA (Low-Rank Adaptation)** to adapt the base architecture to specialized business domains without compromising the model's general intelligence.
+
+### Hyperparameters
+The following hyperparameters were used during the fine-tuning phase:
+
+| Parameter | Value |
+| :--- | :--- |
+| **PEFT Type** | LoRA |
+| **LoRA Rank (r)** | 16 |
+| **LoRA Alpha** | 16 |
+| **LoRA Dropout** | 0.0 |
+| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| **Precision** | bfloat16 |
+| **Optimizer** | AdamW |
+| **Learning Rate** | 2e-4 |
+| **Epochs** | 3 |
+
+### Hardware Requirements
+- **Training:** Single A100 (40GB) or H100 (80GB) recommended. Suitable for consumer GPUs like RTX 3090/4090 using Unsloth 4-bit loading.
+- **Inference:** Minimum 8GB VRAM (Full) / 2GB VRAM (Q8_0 GGUF).
+
+## 9. How to Fine-Tune This Model
+
+If you wish to further adapt **mw-intellix** to your specific organizational data, follow these steps:
+
+1. **Install Dependencies:**
+   ```bash
+   pip install unsloth "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+   pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
+   ```
+
+2. **Load Model with Unsloth:**
+   ```python
+   from unsloth import FastLanguageModel
+   import torch
+
+   model, tokenizer = FastLanguageModel.from_pretrained(
+       model_name = "mediusware-ai/intellix",
+       max_seq_length = 4096,
+       load_in_4bit = True,
+   )
+   ```
+
+3. **Apply LoRA Adapters:**
+   ```python
+   model = FastLanguageModel.get_peft_model(
+       model,
+       r = 16,
+       target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", 
+                        "gate_proj", "up_proj", "down_proj"],
+       lora_alpha = 16,
+       lora_dropout = 0,
+       bias = "none",
+   )
+   ```
+
+4. **Train on Your Data:**
+   Use the `SFTTrainer` from the `trl` library to train on your curated business datasets.
+
+---
+
+## 10. Ethical Considerations
+
+### Data Privacy
+Designed for **Local-First Deployment**. When used via Ollama or GGUF, business data never leaves the local infrastructure, ensuring 100% data residency and privacy.
+
+### Safety Guardrails
+- **Professionalism Filter:** Fine-tuned to avoid informal, casual, or inappropriate language.
+- **Hallucination Mitigation:** Specialized training to prioritize "I don't know" or factual grounding over creative extrapolation in sensitive business contexts.
+
+## 9. Caveats and Recommendations
+
+- **Human-in-the-loop:** While highly accurate, users should always review critical business outputs (e.g., security policies) before implementation.
+- **Language Bias:** Optimized primarily for Business English; performance in other languages may vary.
+
+---
+
+## How to Get Started
+
+### Using with Transformers (PEFT)
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "mediusware-ai/intellix"
+model = AutoModelForCausalLM.from_pretrained("base-model-path")
+model = PeftModel.from_pretrained(model, model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+inputs = tokenizer("Draft a professional email regarding project updates.", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Using with Ollama (GGUF)
+```bash
+ollama run hf.co/mediusware-ai/intellix:Q8_0
+```
+
+---
+
+### Contact & Support
+For custom enterprise deployments or inquiries, visit **[mediusware.com](https://mediusware.com)**.
+
+### Framework Versions
+- PEFT 0.18.1
+- Transformers 4.49.0
+- PyTorch 2.4.0

chat_template.jinja

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+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + 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" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- 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 %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is mapping %}
+                    {%- for args_name in tool_call.arguments %}
+                        {%- set args_value = tool_call.arguments[args_name] %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "IntellixForConditionalGeneration"
+  ],
+  "model_type": "intellix",
+  "base_model_name_or_path": "mediusware/intellix-foundation",
+  "hidden_size": 2048,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 262144,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "vocab_size": 151936,
+  "eos_token_id": 248046,
+  "quantization": {
+    "group_size": 64,
+    "bits": 8,
+    "mode": "affine"
+  },
+  "quantization_config": {
+    "group_size": 64,
+    "bits": 8,
+    "mode": "affine"
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f6fa5470a8b522f3eb43562903e8623fde4ccf5043f9822b83cd689d669e7fe
+size 17750312

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
+size 19989343

tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "processor_class": "IntellixProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}