Instructions to use tanziro/qwen-bangla-crime-investigation-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tanziro/qwen-bangla-crime-investigation-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Use Docker
docker model run hf.co/tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use tanziro/qwen-bangla-crime-investigation-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tanziro/qwen-bangla-crime-investigation-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tanziro/qwen-bangla-crime-investigation-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
- Ollama
How to use tanziro/qwen-bangla-crime-investigation-gguf with Ollama:
ollama run hf.co/tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
- Unsloth Desktop
- Pi
How to use tanziro/qwen-bangla-crime-investigation-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tanziro/qwen-bangla-crime-investigation-gguf with Docker Model Runner:
docker model run hf.co/tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
- Lemonade
How to use tanziro/qwen-bangla-crime-investigation-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Run and chat with the model
lemonade run user.qwen-bangla-crime-investigation-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use tanziro/qwen-bangla-crime-investigation-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tanziro/qwen-bangla-crime-investigation-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "tanziro/qwen-bangla-crime-investigation-gguf:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen Bangla Crime Investigation GGUF
Status: merged GGUF verified and ready
This repository is intended to contain a standalone merged GGUF for LM Studio and other llama.cpp-compatible runtimes.
File
qwen-bangla-crime-investigation-Q4_K_M.gguf: standalone mergedQ4_K_MGGUF for local inference.
Source
- Base model:
Qwen/Qwen3.5-9B - Merge base GGUF:
jc-builds/Qwen3.5-9B-Q4_K_M-GGUF/Qwen3.5-9B-Q4_K_M.gguf - Source LoRA adapter:
tanziro/qwen-bangla-crime-investigation-lora - Source LoRA adapter GGUF:
tanziro/qwen-bangla-crime-investigation-lora-gguf/qwen-bangla-crime-investigation-lora.F16.gguf - Dataset:
tanziro/bangla-crime-investigation-patterns-v2
LM Studio Use
Download qwen-bangla-crime-investigation-Q4_K_M.gguf in LM Studio as a local GGUF model. This repo is different from the LoRA-adapter GGUF repo: this file is merged and does not require loading a separate adapter.
Suggested system prompt:
You are a Bangladesh-focused crime investigation and investigative journalism assistant.
Answer with the final response only. Do not reveal hidden reasoning or step-by-step thinking.
Separate confirmed facts, source claims, and inference.
Do not identify private people from anonymized records.
Avoid doxxing, harassment, vigilantism, or instructions that enable crime or evidence tampering.
Recommend lawful, ethical next steps: document review, source corroboration, official records, expert consultation, right of reply, and personal safety.
Training Notes
The source adapter training run completed 747 of 1200 planned optimizer steps. Logged validation loss was best at step 300 (0.389918), while training loss continued to improve through step 700.
| Step | Training Loss | Validation Loss |
|---|---|---|
| 100 | 0.385574 | 0.409264 |
| 200 | 0.354652 | 0.394317 |
| 300 | 0.356278 | 0.389918 |
| 400 | 0.350144 | 0.391207 |
| 500 | 0.335047 | 0.391896 |
| 600 | 0.318425 | 0.399930 |
| 700 | 0.294148 | 0.406011 |
Known Limitation
The source adapter sometimes emits verbose reasoning traces such as Thinking Process. Use a system prompt that asks for final answers only. Future fine-tuning should include answer-only examples to suppress chain-of-thought style output.
Responsible Use
This model is not a law-enforcement system, legal advisor, or substitute for editorial review. Treat outputs as drafting assistance only. Verify high-stakes claims with documents, official records, independent sources, expert review, and right of reply before publication or action.
Build Provenance
- Build notebook stage:
merged GGUF verified and ready - Last updated:
2026-05-26 23:49:49 UTC - Merge method:
llama-export-lorafrom a Q4_K_M base GGUF, thenllama-quantize --allow-requantize Q4_K_M.
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