Update README.md

README.md

@@ -1,199 +1,241 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- TravelRisk
+- Conflict
+- Finetune
+- ConflictAI
+license: mit
+datasets:
+- Firemedic15/travel-risk-conflict-combined
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen3.5-9B
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Firemedic15/Conflict-Risk-9B-Full
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Conflict-Risk-9B-Full is a fine-tuned language model based on Qwen/Qwen3.5-9B. It was trained on approximately 50,000 records from a combined conflict and travel risk dataset to support risk-aware conversational analysis. The Qwen3.5-9B base model is a 9B-parameter model with native 262,144-token context support, published by Qwen on Hugging Face [web:27].
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The model is intended to help users explore travel risk, conflict context, regional instability, and related analytical questions in a conversational format.
 
-- **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]
+- **Developed by:** Firemedic15
+- **Model type:** Causal language model
+- **Language(s):** English
+- **License:** MIT
+- **Base model:** Qwen/Qwen3.5-9B
+- **Dataset:** Firemedic15/travel-risk-conflict-combined
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Base model:** [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)
+- **Dataset:** [Firemedic15/travel-risk-conflict-combined](https://huggingface.co/datasets/Firemedic15/travel-risk-conflict-combined)
+- **Model repository:** [Firemedic15/Conflict-Risk-9B-Full](https://huggingface.co/Firemedic15/Conflict-Risk-9B-Full)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is designed to support risk-aware dialogue related to travel and conflict. The training objective was to improve performance on prompts involving regional instability, conflict dynamics, and travel-related threat awareness.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Potential direct uses include:
 
-[More Information Needed]
+- Conversational analysis of regional and global conflict conditions
+- Travel risk awareness and qualitative threat discussion
+- Summarization of conflict-related patterns and drivers
+- Research support for risk-focused exploratory tasks
 
-### 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 -->
+Potential downstream uses include:
 
-[More Information Needed]
+- Risk intelligence assistants
+- Travel advisory research prototypes
+- Conflict monitoring and summarization tools
+- Retrieval-augmented applications that combine model output with current external data
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model should not be used for:
 
-[More Information Needed]
+- Real-time emergency response or evacuation decisions
+- Sole-source operational security decisions
+- Military targeting, surveillance, or harmful applications
+- Legal, medical, or life-safety advice
+- Any use case requiring guaranteed real-time factual accuracy without external verification
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model may reflect biases present in the training data, including reporting bias, uneven regional coverage, and differing source quality across conflict and travel datasets.
+
+Because the model was trained to be risk aware, it may overemphasize threat, instability, or negative framing in some contexts. It may also hallucinate specific facts, events, timelines, or causal explanations when asked for highly detailed or current information.
 
-[More Information Needed]
+The model is not a substitute for verified reporting, official advisories, or expert human judgment.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should treat this model as a supporting analytical tool rather than a source of truth.
+
+Recommended safeguards include:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Use current verified data sources for time-sensitive decisions
+- Apply human review before acting on outputs
+- Pair responses with retrieval or external validation where possible
+- Avoid use in high-stakes environments without additional controls
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the example below to load the model with Transformers:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "Firemedic15/Conflict-Risk-9B-Full"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+prompt = "What are the main risk factors affecting travel in a conflict-affected region?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    temperature=0.7,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs, skip_special_tokens=True))
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
+The model was fine-tuned on approximately 50,000 records from the dataset `Firemedic15/travel-risk-conflict-combined`.
 
-[More Information Needed]
+The dataset combines conflict-oriented records and travel risk data to help the model produce more context-aware and risk-sensitive responses.
 
 ### Training Procedure
 
-<!-- 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 from Qwen/Qwen3.5-9B and later merged into a standalone checkpoint for deployment.
 
-#### Preprocessing [optional]
+#### Preprocessing
 
-[More Information Needed]
+Preprocessing likely included:
 
+- Formatting examples into instruction or conversational pairs
+- Normalizing text fields
+- Combining conflict and travel-risk records into a unified training structure
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** bf16 mixed precision
 
-#### Speeds, Sizes, Times [optional]
+Additional hyperparameters such as learning rate, batch size, LoRA rank, number of epochs, and optimizer settings can be added here if available.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+- **Training time:** 26 hours
+- **Training environment:** Local machine
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+### Testing Data, Factors, and Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+Evaluation data has not yet been fully documented. A recommended evaluation setup would include held-out prompts covering conflict analysis, regional instability, and travel-risk reasoning.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Relevant evaluation factors may include:
 
-[More Information Needed]
+- Geographic region
+- Conflict severity
+- Prompt wording and complexity
+- Time sensitivity
+- Degree of uncertainty in the prompt
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- Accuracy
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+Formal benchmark results are not yet documented in this card.
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model is intended to improve risk-aware reasoning over the base model for conflict and travel-risk related prompts, but outputs should still be validated by human review and external sources where reliability matters.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-- **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]
+- **Hardware Type:** NVIDIA GeForce RTX 5070 Ti Blackwell
+- **Hours used:** 26
+- **Cloud Provider:** Local machine
+- **Compute Region:** Local
+- **Carbon Emitted:** Not estimated
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+This model is a fine-tuned causal language model based on Qwen/Qwen3.5-9B. According to the base model page, Qwen/Qwen3.5-9B is a 9B-parameter model with 32 layers and a native context length of 262,144 tokens [web:27]. Its objective in this fine-tuned form is to provide improved performance on prompts related to conflict awareness, travel risk, and risk-informed dialogue.
 
 ### Compute Infrastructure
 
-[More Information Needed]
+The model was trained locally on consumer GPU hardware for approximately 26 hours.
 
 #### Hardware
 
-[More Information Needed]
+- NVIDIA GeForce RTX 5070 Ti Blackwell
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- Transformers
+- PyTorch
+- PEFT
+- Accelerate
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{firemedic15conflictrisk9bfull,
+  title        = {Conflict-Risk-9B-Full},
+  author       = {Firemedic15},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/Firemedic15/Conflict-Risk-9B-Full}},
+  note         = {Fine-tuned from Qwen/Qwen3.5-9B on combined conflict and travel-risk data}
+}
+```
 
 **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]
+Firemedic15. (2026). *Conflict-Risk-9B-Full* [Computer software]. Hugging Face. https://huggingface.co/Firemedic15/Conflict-Risk-9B-Full
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+This model is best used as part of a broader analytical workflow that includes current data sources, human review, and domain-specific validation.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Firemedic15
 
 ## Model Card Contact
 
-[More Information Needed]
+Firemedic15 via Hugging Face Hub