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.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

@@ -1,252 +1,210 @@
 ---
-license: apache-2.0
-base_model:
-- Qwen/Qwen3-0.6B
+base_model: unsloth/qwen3-0.6b-bnb-4bit
+library_name: peft
 pipeline_tag: text-generation
 tags:
-- cybersecurity
-- siem
-- log-analysis
-- field-extraction
-- security-automation
-- fine-tuned
+- base_model:adapter:unsloth/qwen3-0.6b-bnb-4bit
+- lora
+- sft
+- transformers
+- trl
+- unsloth
 ---
 
-# LLMSIEM/logem
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-LLMSIEM/logem is a specialized language model fine-tuned for Security Information and Event Management (SIEM) tasks, particularly excelling at structured field extraction from security logs and events.
 
 ## Model Details
 
 ### Model Description
 
-LLMSIEM/logem is a fine-tuned version of Qwen3-0.6B, specifically optimized for cybersecurity applications. The model demonstrates that targeted fine-tuning can dramatically improve performance on domain-specific tasks, achieving superior results compared to much larger general-purpose models.
+<!-- Provide a longer summary of what this model is. -->
+
 
-- **Developed by:** [Your Name/Organization]
-- **Model type:** Causal Language Model (Fine-tuned)
-- **Language(s):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** Qwen/Qwen3-0.6B
-- **Model size:** 1.2 GB (FP16), 396 MB (Q4_K_M quantized)
-- **Parameters:** 0.6B
 
-### Model Sources
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-- **Repository:** [Your GitHub Repository]
-- **Paper:** [Research Paper Link if available]
-- **Blog Post:** [LinkedIn/Blog Series Link]
+### Model Sources [optional]
 
-## Performance Highlights
+<!-- Provide the basic links for the model. -->
 
-🏆 **Best-in-class performance** for SIEM field extraction tasks:
-- **66.7% perfect matches** (FP16 version)
-- **0.833 F1 score** - outperforms 12B parameter models
-- **1.00s average response time** - 3x faster than larger alternatives
-- **Zero complete failures** on standardized test suite
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-### Direct Use
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-The model is designed for cybersecurity professionals and SIEM engineers who need to:
-- Extract structured fields from security logs
-- Parse and normalize security event data
-- Automate log analysis workflows
-- Generate structured outputs from unstructured security data
+### Direct Use
 
-### Example Use Cases
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-```python
-# Example: Extract fields from a security log
-input_text = "Extract fields from: Failed login attempt from 192.168.1.100 for user admin at 2024-01-15T10:30:45Z"
+[More Information Needed]
 
-# Model will output structured JSON with relevant fields:
-# {
-#   "event_type": "failed_login",
-#   "source_ip": "192.168.1.100", 
-#   "username": "admin",
-#   "timestamp": "2024-01-15T10:30:45Z"
-# }
-```
+### Downstream Use [optional]
 
-### Downstream Use
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-- Integration into SIEM platforms (Splunk, ELK, QRadar)
-- Security orchestration and automated response (SOAR) workflows
-- Threat hunting and incident response automation
-- Security data lake processing pipelines
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-- General-purpose text generation
-- Non-security related field extraction
-- Real-time processing without proper input validation
-- Decision-making for critical security responses without human oversight
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-### Technical Limitations
-- Optimized specifically for security log formats seen during training
-- May struggle with completely novel log formats or schemas
-- Performance may degrade on logs with unusual encoding or formatting
-- Quantized version (Q4_K_M) shows 5% accuracy reduction vs FP16
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-### Security Considerations
-- Model outputs should be validated before use in automated security workflows
-- Not suitable for real-time critical security decisions without human oversight
-- Training data may contain biases from specific security environments
-- Should not be the sole source of truth for security incident classification
+[More Information Needed]
 
 ### Recommendations
 
-- Always validate model outputs in production security environments
-- Implement fallback mechanisms for handling novel or malformed inputs
-- Regular retraining recommended as new log formats emerge
-- Use FP16 version for maximum accuracy, Q4_K_M for resource-constrained deployments
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-## How to Get Started with the Model
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-# Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("LLMSIEM/logem")
-model = AutoModelForCausalLM.from_pretrained("LLMSIEM/logem")
+## How to Get Started with the Model
 
-# Example usage
-prompt = "Extract security fields from the following log: [your log here]"
-inputs = tokenizer(prompt, return_tensors="pt")
+Use the code below to get started with the model.
 
-with torch.no_grad():
-    outputs = model.generate(
-        inputs.input_ids,
-        max_length=512,
-        temperature=0.1,
-        do_sample=False,
-        pad_token_id=tokenizer.eos_token_id
-    )
+[More Information Needed]
 
-result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(result)
-```
+## Training Details
 
-### Using with Ollama (Recommended for Production)
+### Training Data
 
-```bash
-# Pull the quantized version
-ollama pull LLMSIEM/logem
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-# Run inference
-ollama run LLMSIEM/logem "Extract fields from: SSH login from 10.0.0.5 by root"
-```
+[More Information Needed]
 
-## Training Details
+### Training Procedure
 
-### Training Data
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-The model was fine-tuned on a curated dataset of security logs and corresponding structured field extractions, including:
-- Network security events (firewall, IDS/IPS)
-- Authentication logs (successful/failed logins)
-- System security events (file access, process execution)
-- Application security logs (web servers, databases)
+#### Preprocessing [optional]
 
-Dataset characteristics:
-- 21 standardized test cases for evaluation
-- Diverse log formats and security event types
-- JSON-formatted target outputs for structured field extraction
+[More Information Needed]
 
-### Training Procedure
 
 #### Training Hyperparameters
 
-- **Base model:** Qwen3-0.6B
-- **Training regime:** Mixed precision (fp16)
-- **Fine-tuning approach:** Supervised fine-tuning on field extraction tasks
-- **Optimization:** Task-specific training for SIEM applications
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Model Variants
+#### Speeds, Sizes, Times [optional]
 
-- **FP16 Version:** 1.2 GB, maximum accuracy (0.833 F1)
-- **Q4_K_M Quantized:** 396 MB, production-optimized (0.800 F1)
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
 
 ## Evaluation
 
+<!-- This section describes the evaluation protocols and provides the results. -->
+
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
-- 21 standardized security log parsing test cases
-- Diverse log formats from multiple security tools
-- Ground truth structured outputs for comparison
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
 
 #### Metrics
-- **Perfect Match Rate:** Percentage of test cases with 100% accurate field extraction
-- **F1 Score:** Harmonic mean of precision and recall for field detection
-- **Precision:** Accuracy of extracted fields
-- **Response Time:** Average inference latency
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
 
 ### Results
 
-| Model | Perfect Matches | Avg F1 | Precision | Speed | Size |
-|-------|----------------|---------|-----------|-------|------|
-| **LLMSIEM/logem (FP16)** | **14/21 (66.7%)** | **0.833** | **0.848** | **1.00s** | **1.2 GB** |
-| LLMSIEM/logem (Q4_K_M) | 13/21 (61.9%) | 0.800 | 0.819 | 1.00s | 396 MB |
-| Gemma:12B | 15/21 (71.4%) | 0.790 | 0.788 | 3.06s | 5.0 GB |
-| Qwen3:0.6B (base) | 9/21 (42.9%) | 0.651 | 0.636 | 1.57s | 522 MB |
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
 
-#### Key Findings
-- **+28% F1 improvement** over base Qwen3-0.6B model
-- **Outperforms 12B models** in F1 score despite being 20x smaller
-- **3x faster** than comparable accuracy models
-- **12.6x smaller** than Gemma while maintaining superior performance
+[More Information Needed]
 
 ## Environmental Impact
 
-Training a specialized 0.6B parameter model requires significantly less computational resources compared to training larger models from scratch:
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-- **Hardware Type:** NVIDIA GPU (specific details TBD)
-- **Training approach:** Fine-tuning (more efficient than training from scratch)
-- **Base model efficiency:** Starting from pre-trained Qwen3-0.6B reduces carbon footprint
-- **Production efficiency:** Smaller model size reduces inference energy consumption
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-## Technical Specifications
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
 
-### Model Architecture
-- **Architecture:** Transformer decoder (Qwen3 family)
-- **Parameters:** 0.6 billion
-- **Context length:** [Inherited from Qwen3-0.6B]
-- **Vocabulary size:** [Inherited from Qwen3-0.6B]
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
 
 ### Compute Infrastructure
-- **Training:** Fine-tuning on security-specific datasets
-- **Inference:** Optimized for CPU and GPU deployment
-- **Quantization:** GGML Q4_K_M for edge deployment
 
-## Citation
+[More Information Needed]
 
-If you use this model in your research or applications, please cite:
+#### Hardware
 
-```bibtex
-@misc{llmsiem-logem-2024,
-  title={LLMSIEM/logem: A Fine-tuned Language Model for Security Log Analysis},
-  author={[Your Name]},
-  year={2024},
-  url={https://huggingface.co/LLMSIEM/logem},
-  note={Fine-tuned from Qwen3-0.6B for SIEM applications}
-}
-```
+[More Information Needed]
 
-## Model Card Authors
+#### Software
 
-[Your Name/Organization]
+[More Information Needed]
 
-## Model Card Contact
+## Citation [optional]
 
-For questions about this model, please contact:
-- **Email:** [your-email@domain.com]
-- **LinkedIn:** [Your LinkedIn Profile]
-- **GitHub:** [Your GitHub Profile]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
----
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
 
-*This model is part of the LLMSIEM research series exploring the application of Large Language Models in cybersecurity and SIEM workflows.*
+- PEFT 0.17.0

adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/qwen3-0.6b-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "o_proj",
+    "gate_proj",
+    "q_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b40ed4316708bff050f2cc842c43fc21962c03ed62257e500bf4ea7aaa1a53f6
+size 40422168

added_tokens.json

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+  "</tool_call>": 151658,
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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 defined and 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 m_content = message.content if message.content is defined and message.content is not none else '' %}
+        {%- set content = m_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 m_content %}
+                {%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (m_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 (not reasoning_content.strip() == '')) %}
+                {{- '<|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 %}

special_tokens_map.json

@@ -0,0 +1,31 @@
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+    "lstrip": false,
+    "normalized": false,
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+    "content": "<|vision_pad|>",
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+    "normalized": false,
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+    "single_word": false
+  }
+}

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+size 11422654

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+    },
+    "151657": {
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+      "normalized": false,
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+    },
+    "151658": {
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+      "normalized": false,
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+      "normalized": false,
+      "rstrip": false,
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+    },
+    "151664": {
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+      "normalized": false,
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+    },
+    "151665": {
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+      "normalized": false,
+      "rstrip": false,
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+    },
+    "151666": {
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+      "normalized": false,
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+    "151668": {
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+    }
+  },
+  "additional_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|>"
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+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 40960,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}