This GGUF variant is intended for local inference via llama.cpp, Ollama, LM Studio, and compatible runtimes — no GPU required for most quantization levels.
This model is built for security practitioners, travel risk analysts, and OSINT researchers who need fast, structured responses to questions like:
What is the current conflict situation in [country]?
What travel advisory level applies to [region]?
What are the primary security risks for a traveler with [passport country] visiting [destination]?
Summarize recent armed conflict events in [area] over the last 30 days.
It is deployed as the inference backend for the Firemedic15/OSINTTool Gradio Space, which combines this model with live data from ACLED, State Department advisories, RSS feeds, and airspace sources via a smolagents agentic loop.
This model is not suitable for:
Medical, legal, or financial decisions
Operational security planning without human verification
Any context requiring real-time situational awareness (the model has a training cutoff and no live data access on its own)
Available Files
File
Quantization
Size (approx.)
Recommended For
Qwen2.5_Risk_1.5B.gguf
F16 (full precision)
~3.0 GB
GPU inference, maximum accuracy
Qwen2.5_Risk_1.5B-Q4_K_M.gguf
Q4_K_M
~0.9 GB
CPU inference, laptops, low RAM
Which should you use?
If you have a GPU or want the highest fidelity output — use the F16 file.
If you're running on a CPU, a laptop, or have less than 4 GB of RAM to spare — use Q4_K_M. For a 1.5B model the quality difference is minimal in practice.
Quickstart
Ollama
Option A — Pull directly from HuggingFace (no download needed):
ollama run hf.co/Firemedic15/Qwen2.5_Risk_1.5B
Option B — Install a local GGUF file:
This is useful if you've already downloaded the file or want to control which quantization you run.
Create a file named Modelfile (no extension) in the same directory as your GGUF:
FROM ./Qwen2.5_Risk_1.5B-Q4_K_M.gguf
SYSTEM """You are a professional OSINT intelligence analyst specializing in geopolitical conflict and security threat assessment. Provide structured, factual risk assessments based on the information available to you."""
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 4096
Swap Qwen2.5_Risk_1.5B-Q4_K_M.gguf for the F16 filename if you downloaded that one instead.
Step 3 — Register the model with Ollama
ollama create qwen-risk -f Modelfile
Step 4 — Run it
ollama run qwen-risk
Once running, query it directly:
ollama run qwen-risk "What are the primary security risks for travelers in Sudan?"
Or use the OpenAI-compatible API that Ollama exposes:
python
1import requests
23response = requests.post(4"http://localhost:11434/api/chat",5 json={6"model":"qwen-risk",7"messages":[8{"role":"user","content":"Assess the travel risk for Ukraine right now."}9],10"stream":False,11}12)13print(response.json()["message"]["content"])
1llama-server -hf Firemedic15/Qwen2.5_Risk_1.5B
2# Exposes http://localhost:8080/v1 — compatible with any OpenAI client
llama-cpp-python
python
1from llama_cpp import Llama
23# F16 — full precision, best for GPU4llm = Llama.from_pretrained(5 repo_id="Firemedic15/Qwen2.5_Risk_1.5B",6 filename="Qwen2.5_Risk_1.5B.gguf",7)89# Q4_K_M — quantized, best for CPU / low RAM10llm = Llama.from_pretrained(11 repo_id="Firemedic15/Qwen2.5_Risk_1.5B",12 filename="Qwen2.5_Risk_1.5B-Q4_K_M.gguf",13)1415response = llm.create_chat_completion(16 messages=[17{18"role":"system",19"content":"You are a professional OSINT analyst specializing in geopolitical travel risk assessment."20},21{22"role":"user",23"content":"What are the primary security risks for travelers in Sudan right now?"24}25],26 max_tokens=512,27 temperature=0.7,28)29print(response["choices"][0]["message"]["content"])
Docker
docker model run hf.co/Firemedic15/Qwen2.5_Risk_1.5B
LM Studio / Jan
Search for Firemedic15/Qwen2.5_Risk_1.5B in the model browser and download directly.
Prompt Format
This model uses the Qwen2.5 chat template. Structure your prompts as:
<|im_start|>system
You are a professional OSINT intelligence analyst specializing in geopolitical conflict and security threat assessment.
<|im_end|>
<|im_start|>user
[Your question here]
<|im_end|>
<|im_start|>assistant
For best results, include relevant context (country, timeframe, traveler profile) in the user turn rather than relying on the model's training data alone.
Training Details
Base: Qwen2.5-1.5B-Instruct
Method: Supervised fine-tuning (SFT) via HuggingFace TRL + PEFT
No live data. The model has a training cutoff and does not fetch real-time conflict or advisory data on its own. For live analysis, use the OSINTTool Space which combines this model with live data pipelines.
1.5B scale. Tool-calling and structured JSON output are shallower than larger models. Outputs should be treated as analyst assistance, not authoritative assessments.
English only. Fine-tuned on English-language data. Performance on non-English queries is not validated.
Not for operational use without verification. AI-generated risk assessments from open sources are a starting point, not a finished product.
