update model card 2

README.md

@@ -10,6 +10,11 @@ tags:
 - cti
 - ner
 - information-extraction
+license: apache-2.0
+datasets:
+- mrmoor/cyber-threat-intelligence
+language:
+- en
 ---
 
 # Model Card for Model ID
@@ -23,7 +28,7 @@ It transforms raw, technical text into structured JSON format containing cyberse
 ### Model Description
 
 This model uses QLoRA (Quantized Low-Rank Adaptation) to efficiently adapt the Mistral-7B base model for the highly specific task of Named Entity Recognition (NER) in the cybersecurity domain. 
-The model outputs a strict JSON structure, making it ideal for integration into automated RAG pipelines, SIEMs, or autonomous agent workflows (like LangGraph).
+The model outputs a strict JSON structure, making it ideal for integration into automated RAG pipelines or autonomous agent workflows (like LangGraph).
 
 - **Developed by:** Alex Bueno
 - **Model type:** Causal Language Model with LoRA adapters (PEFT)
@@ -31,7 +36,7 @@ The model outputs a strict JSON structure, making it ideal for integration into
 - **License:** Apache 2.0
 - **Finetuned from model:** `mistralai/Mistral-7B-v0.3`
 
-### Model Sources [optional]
+### Model Sources
 
 - **Repository:** https://huggingface.co/AlexXBueno/Mistral-7B-Cyber-Thread-Intelligence-Extractor
 
@@ -45,7 +50,7 @@ It will extract relevant entities and return them as a structured JSON array.
 
 ### Downstream Use
 
-- **Multi-Agent Systems:** As a specific Tool Node for an orchestrator agent (e.g., Llama-3-70B) to extract structured data before querying a Vector Database or SQL.
+- **Multi-Agent Systems:** As a specific Tool Node for an orchestrator agent to extract structured data before querying a Vector Database or SQL.
 - **CTI Pipelines:** Automated ingestion and structuring of daily threat reports into a local database.
 
 
@@ -55,7 +60,7 @@ The model may suffer from previous knowledge bias, which may leads to insert thr
 
 ### Recommendations
 
-- **Temperature:** It is strictly recommended to use a low temperature (`temperature=0.1` or `0.0`) during inference to ensure deterministic extraction.
+- **Temperature:** It is recommended to use a low temperature (`temperature=0.1` or `0.0`) during inference to ensure deterministic extraction.
 - **Validation:** Use Pydantic or structured decoding libraries (like `Outlines` or `Guidance`) in production to enforce JSON grammar, as the model may occasionally produce malformed JSON syntax.
 
 
@@ -123,12 +128,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response
 
 ### Training Data
 
-The model was fine-tuned on the ```mrmoor/cyber-threat-intelligence``` dataset, which contains annotated cybersecurity entities.
+The model was fine-tuned on the `mrmoor/cyber-threat-intelligence` dataset, which contains annotated cybersecurity entities.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
 #### Preprocessing
 
 A custom Data Collator (```CTICompletionCollator```) was implemented during training. 
@@ -157,7 +160,7 @@ The objective is strictly Information Extraction (IE) formatted as an Instructio
 
 ### Compute Infrastructure
 
-The entire stack was developed and validated on local/on-premise infrastructure, bypassing cloud dependencies to assure data privacy for sensitive CTI documents.
+The entire stack was developed and validated on local infrastructure, avoiding cloud dependencies to esnure data privacy for sensitive CTI documents.
 
 #### Software
 - PEFT 0.18.1