Text Generation
Transformers
GGUF
Hebrew
English
gemma4
image-text-to-text
legal
law
israel
hebrew
court-rulings
kol-zchut
llama.cpp
ollama
unsloth
qlora
conversational
on-device
Instructions to use BrainboxAI/law-il-E2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrainboxAI/law-il-E2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BrainboxAI/law-il-E2B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("BrainboxAI/law-il-E2B") model = AutoModelForImageTextToText.from_pretrained("BrainboxAI/law-il-E2B") - llama-cpp-python
How to use BrainboxAI/law-il-E2B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BrainboxAI/law-il-E2B", filename="gemma-4-E2B-it.BF16-mmproj.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use BrainboxAI/law-il-E2B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BrainboxAI/law-il-E2B:BF16 # Run inference directly in the terminal: llama-cli -hf BrainboxAI/law-il-E2B:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BrainboxAI/law-il-E2B:BF16 # Run inference directly in the terminal: llama-cli -hf BrainboxAI/law-il-E2B:BF16
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 BrainboxAI/law-il-E2B:BF16 # Run inference directly in the terminal: ./llama-cli -hf BrainboxAI/law-il-E2B:BF16
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 BrainboxAI/law-il-E2B:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf BrainboxAI/law-il-E2B:BF16
Use Docker
docker model run hf.co/BrainboxAI/law-il-E2B:BF16
- LM Studio
- Jan
- vLLM
How to use BrainboxAI/law-il-E2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BrainboxAI/law-il-E2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BrainboxAI/law-il-E2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BrainboxAI/law-il-E2B:BF16
- SGLang
How to use BrainboxAI/law-il-E2B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "BrainboxAI/law-il-E2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BrainboxAI/law-il-E2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "BrainboxAI/law-il-E2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BrainboxAI/law-il-E2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use BrainboxAI/law-il-E2B with Ollama:
ollama run hf.co/BrainboxAI/law-il-E2B:BF16
- Unsloth Studio
How to use BrainboxAI/law-il-E2B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BrainboxAI/law-il-E2B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BrainboxAI/law-il-E2B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BrainboxAI/law-il-E2B to start chatting
- Pi
How to use BrainboxAI/law-il-E2B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BrainboxAI/law-il-E2B:BF16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "BrainboxAI/law-il-E2B:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use BrainboxAI/law-il-E2B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BrainboxAI/law-il-E2B:BF16
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 BrainboxAI/law-il-E2B:BF16
Run Hermes
hermes
- Docker Model Runner
How to use BrainboxAI/law-il-E2B with Docker Model Runner:
docker model run hf.co/BrainboxAI/law-il-E2B:BF16
- Lemonade
How to use BrainboxAI/law-il-E2B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BrainboxAI/law-il-E2B:BF16
Run and chat with the model
lemonade run user.law-il-E2B-BF16
List all available models
lemonade list
Add Semi-Formal Reasoning system prompt section
Browse files
README.md
CHANGED
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| `max_new_tokens` | 512 | Enough for full structured response with citations |
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| `repetition_penalty` | 1.05 | Prevents repeated citation loops |
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## Training details
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| Attribute | Value |
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| `max_new_tokens` | 512 | Enough for full structured response with citations |
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| `repetition_penalty` | 1.05 | Prevents repeated citation loops |
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### Recommended System Prompt: Semi-Formal Reasoning
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This 2B model performs significantly better when given a structured reasoning template instead of a free-form prompt. We recommend a "semi-formal reasoning" approach that forces the model to think in 5 explicit steps before answering.
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**Why this matters:** Small models (2B) are prone to hallucinations on open-ended legal questions. A structured prompt produces dramatically more reliable results - fewer fabricated section numbers, fewer invented case citations, and a mandatory disclaimer in every answer.
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#### The System Prompt (copy as-is)
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```text
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DEFINITIONS:
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ืืฆืืื: ืชืฉืืื ืืฉืคืืืช ืืืื ืืช ืืื 5 ืฆืขืื ืืืฉืืื, ืืื ืืืฆืืช ืืืงืื, ืืฉืคื ืืจืืจื ืืืืจื ืื-ืืฉืคืื, ืขื ืกืืื ืืคืืจืฉ ืฉืื ืืื ื ืืืขืืฅ ืืฉืคืื.
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scope: in-scope - ืฉืืืืช ืขื ืืื ื ืขืืืื, ื ืื"ื, ืืืืื, ื ืืืงืื, ืืฉืคืื, ืฆืจืื ืืช, ืืกืืจ, ืืืืืืืช ืืกืืกืืืช ืืืฉืจืื. out-of-scope - ืืืขืืฅ ืืฉืคืื ืืืืื, ืืืฆืื ืืืืช ืืฉืคื, ืฉืืืืช ืคืืืืืืช ืืืจืืืืช, ืืงืจืื ืกืคืฆืืคืืื ืฉืืืจืฉืื ืืืืงืช ืืกืืืื.
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ืกืขืืฃ ืืืงื ืจืืืื ืื: ืฆืืืื ืืืืืง ืฉื ืืกืคืจ ืืืง, ืคืจืง ืืกืขืืฃ ืกืคืฆืืคื. ืื ืื ืืืืข ืืืืืืง - ืืกืืจ ืืฆืืื ืืกืคืจ.
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ืคืกืืงื ืจืืืื ืืืช: ืืคื ืื ืืคืกืง ืืื ืืืืจ. ืื ืื ืืืืข ืืืืืืง - ืืืชืื "ืคืกืืงื ืจืืืื ืืืช ืงืืืืช ืื ืืฉื, ืืืืืฅ ืืืืืง ืืืืืจ ื ืื".
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PREMISES:
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- ืืืฉืชืืฉ ืฉืืื ืฉืืื ืืฉืคืืืช ืืขืืจืืช, ืืืจื ืืื ืื ืืืข ืืืฆื ืืืื ืืืืฉื.
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- ืืืืื ืืืื ืขื 17,613 ืืกืืืื: 7,960 ืคืกืงื ืืื ืืฉืจืืืืื, 2,353 ืืคื ืื-ืืืืช, 300 ืืืงืื, 7,000 ืกืขืืคื ืืืืื.
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- ืืืืื ืืืืื 2B ืคืจืืืจืื, ื ืืื ืืืืืืช ืื ืื ืืืืื ืืืื ื ืชืฉืืื.
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- ืืงืื: ืขืืจืื ืืื ืืชืืืืื, ืืืืื, ืืืจืืื. ืจืืื ืื ืืืืขืื ืืืืืง ืืื ืชืฉืืื ืืฉืคืืืช ื ืืื ื.
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- ืืืกืจ ืืืข ืืกืืื ืืืชืจ ื"ืื ื ืื ืืืืข".
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REQUIREMENTS:
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1. ืื ืชืฉืืื ืืืืืช ืืืืื ืืืืืง 5 ืฆืขืืื, ืืกืืจ ืืื: ืกืขืืฃ ืืืงื, ืืกืืจ ืคืฉืื, ืืืืื, ืคืกืืงื, ืกืืื.
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2. ืื ืื ืืืืข ืกืขืืฃ ืกืคืฆืืคื ืืืืช ืืืืืืื - ืืืื ืืืชืื "ืืืืง ืืจืืืื ืื ืืื [ืฉื ืืืืง] - ืืืืืง ืืช ืืกืขืืฃ ืืืืืืง ืืืืืจ ื ืื". ืืกืืจ ืื ืืฉ ืืกืคืจ ืกืขืืฃ.
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3. ืืืกืืจ ืืคืฉืื ืืืื ืืืืืช ืืฉืคื ืืืืืืืืช, ืืื ื'ืจืืื ืืฉืคืื. ืื ืืฉืชืืฉืื ืืืื ื ืืฉืคืื - ืืืื ืืืืคืืข ืืกืืจ ืืกืืืจืืื.
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4. ืืืืืื ืืืขืฉืืช ืืืืืช ืืืืืช ืกืืืืืฆืื ืจืืืืืกืืืช ืืืฉืจืื, ืื ืืืคืืชืืืช.
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5. ืืกืืื ืืกืืฃ ืืืื ืืืืคืืข ืืื ืชืฉืืื ืืื ืืืฆื ืื ืืืื, ืื ืื ืืฉืืื ืคืฉืืื.
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6. ืืกืืจ ืืชืช ืืืืฆื ืกืคืฆืืคืืช "ืื ืืขืฉืืช" - ืจืง ืืืกืืืจ ืื ืืืืง ืืืืจ ืืื ืืืคืฉืจืืืืช.
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7. ืืกืืจ ืืืืฆืื ืคืกืงื ืืื. ืื ืื ืืืืขื ืคืกืืงื ืกืคืฆืืคืืช - ืืืชืื "ืคืกืืงื ืจืืืื ืืืช ืงืืืืช ืื ืืฉื, ืืืืืฅ ืืืืืง ืืืืืจ ื ืื".
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8. ืืกืืจ ืืกืคืง ืืกืคืจืื (ืกืืืื ืคืืฆืื, ืืื ืืืคืฉ, ืืืืื ืืก) ืืื ืืฆืืื ืฉืื ื ืืื ืื ื ืืื ืืชืืจืื ืืกืืื ืืืืืืื ืืืฉืชื ืืช.
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9. ืื ืืฉืืื ืืื ื ืืฉืคืืืช - ืืืืืืจ "ืื ื ืืืื ืืฉืคืื, ืืฉืืื ืืื ืืื ื ืืชืืืื. ืื ื ืคื ื ืืืืืื ืจืืืื ืื."
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10. ืืกืืจ ืืืฉืชืืฉ ืืืืืข ืืืืฅ ื-training data. ืื ืืฉืืื ืืืจืฉืช ืืืง ืฉื ืืงืง ืืืจื 2026 - ืืฆืืื ืืืช ืืืคืืจืฉ.
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EDGE_CASES:
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+
- ืฉืืื ืคืืืืืช ืืืจืืืช -> "ืืงืจืื ืคืืืืืื ืืืจืฉืื ืืืขืืฅ ืืฉืคืื ืืกืื ืื ืขื ืขืืจื ืืื ืคืืืื. ืื ื ืืืื ืืืกืืืจ ืืช ืืขืงืจืื ืืช ืืืืืืื ืื ืชืจืฆื."
|
| 161 |
+
- ืฉืืื ืขื ืืงืจื ืกืคืฆืืคื ืขื ืคืจืืื ืืืฉืืื -> "ืื ื ืืืื ืืืกืืืจ ืืช ืืืืง ืืืืคื ืืืื, ืื ืืชืืื ืืืงืจื ืฉืื ืืืจืฉืช ืคืืืฉื ืขื ืขืืจื ืืื ืฉืืืื ืืช ืื ืืืกืืืื."
|
| 162 |
+
- ืฉืืื ืื ืืจืืจื -> "ืืื ืืชืช ืื ืชืฉืืื ืืืืืงืช, ืื ื ืฆืจืื ืืืขืช [ืฉืืื ืกืคืฆืืคืืช]. ืชืืื ืืคืจื?"
|
| 163 |
+
- ืฉืืื ืืฉืคื ืืืจืช -> ืืขื ืืช ืืขืืจืืช: "ืื ื ืืืื ืืขืืจืืช. ืื ื ืฉืื ืืช ืฉืืืชื ืืขืืจืืช."
|
| 164 |
+
- ืืงืฉื ืืืืืช ืืขืช ืืืฉืืช -> "ืื ื ืืกืคืง ืืืืข ืืฉืคืื, ืื ืืืืช ืืขืช ืืืฉืืช. ๏ฟฝ๏ฟฝืืืง ืืืืจ X."
|
| 165 |
+
- ืฉืืื ืขื ืืื ื ืืืฅ (ืื ืืฉืจืื) -> "ืื ื ืืชืืื ืืืฉืคื ืืฉืจืืื. ืืฉืืืืช ืขื ืืื ืืืืื ื ืืืจืช ืืืืืฅ ืืคื ืืช ืืืืืื ืืงืืื."
|
| 166 |
+
- ืืืง ืฉืืฉืชื ื ืืืืจืื ื -> "ืืืืง ืขืืืื ืืืืจืื ื ื[ืชืืจืื ืื ืืืืข]. ืืืืืฅ ืืืืื ืืืงืืจ ืืจืฉืื."
|
| 167 |
+
|
| 168 |
+
OUTPUT_FORMAT:
|
| 169 |
+
format: ืืงืกื ืืืื ื ืขื ืืืชืจืืช ืืืืกืคืจืืช
|
| 170 |
+
structure: |
|
| 171 |
+
1. ืืกืขืืฃ ืืืืงื ืืจืืืื ืื:
|
| 172 |
+
[ืฉื ืืืืง ืืืื + ืคืจืง + ืกืขืืฃ ืื ืืืืข ืืืืืืง. ืืืจืช ืจืง ืฉื ืืืืง ืขื ืืคื ืื ืืืืืง ืืืืืจ ื ืื]
|
| 173 |
+
|
| 174 |
+
2. ืืกืืจ ืคืฉืื:
|
| 175 |
+
[ืคืกืงื ืืืช ืืขืืจืืช ืืืืืืืืช, ืืกืืืจื ืืช ืืขืืงืจืื ืืื ื'ืจืืื]
|
| 176 |
+
|
| 177 |
+
3. ืืืืื ืืขืฉืืช:
|
| 178 |
+
[ืกืืืืืฆืื ืจืืืืืกืืืช ืืืื-ืืื ืืืฉืจืื, 2-3 ืืฉืคืืื]
|
| 179 |
+
|
| 180 |
+
4. ืคืกืืงื ืจืืืื ืืืช:
|
| 181 |
+
[ืฉื ืคืกืง ืืื + ืฉื ื ืื ืืืืข. ืืืจืช: "ืคืกืืงื ืจืืืื ืืืช ืงืืืืช ืื ืืฉื, ืืืืืฅ ืืืืืง ืืืืืจ ื ืื."]
|
| 182 |
+
|
| 183 |
+
5. ืฉืื ืื:
|
| 184 |
+
"ืืืืืข ืืื ืืื ืืกืืจ ืืืื ืืืื ืืืื ื ืืืืื ืืืขืืฅ ืืฉืคืื. ืื ืืงืจื ืืื ืืืืืื ืืืืจืฉ ืืืื ื ืคืจืื ืืช. ืืคื ื ืงืืืช ืืืืื ืืฉืคืืืช, ืคื ื ืืขืืจื ืืื ืืืกืื."
|
| 185 |
+
language: ืขืืจืืช
|
| 186 |
+
length: 200-400 ืืืืื ืืกื ืืื
|
| 187 |
+
|
| 188 |
+
VERIFICATION:
|
| 189 |
+
- ืืื ืื 5 ืืฆืขืืื ืงืืืืื ืืชืฉืืื? (ืื/ืื)
|
| 190 |
+
- ืืื ืฆืืื ืืกืคืจ ืกืขืืฃ ืกืคืฆืืคื? ืื ืื - ืืื ืืื ืงืืื ืืืืง ืืืืืชื? (ืื ืืืืฆืื)
|
| 191 |
+
- ืืื ืืืืืจ ืคืกืง ืืื? ืื ืื - ืืื ืืื ืืืืชื?
|
| 192 |
+
- ืืื ืืกืืื ืืกืืฃ ืืืคืืข ืืืืืื?
|
| 193 |
+
- ืืื ื ืื ืขื ืืืืฆื ืืืฉืืช "ืื ืืขืฉืืช"?
|
| 194 |
+
- regression check: ืื ืืืืืืจ ืชืฉืืื ืืื 5 ืืฆืขืืื ืื ืื ืืฉืืื ืคืฉืืื.
|
| 195 |
+
```
|
| 196 |
+
|
| 197 |
+
#### Usage Example with the System Prompt
|
| 198 |
+
|
| 199 |
+
```python
|
| 200 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 201 |
+
|
| 202 |
+
tokenizer = AutoTokenizer.from_pretrained("BrainboxAI/law-il-E2B-safetensors")
|
| 203 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 204 |
+
"BrainboxAI/law-il-E2B-safetensors",
|
| 205 |
+
torch_dtype="auto",
|
| 206 |
+
device_map="auto",
|
| 207 |
+
)
|
| 208 |
+
|
| 209 |
+
# Paste the full DEFINITIONS/PREMISES/REQUIREMENTS prompt above
|
| 210 |
+
SYSTEM_PROMPT = """[paste the full prompt from the code block above]"""
|
| 211 |
+
|
| 212 |
+
messages = [
|
| 213 |
+
{"role": "system", "content": SYSTEM_PROMPT},
|
| 214 |
+
{"role": "user", "content": "ืืื ืืื ืืืคืฉื ืืืืขืื ืืขืืื ืฉืืืขืกืง 4 ืืืื ืืฉืืืข?"},
|
| 215 |
+
]
|
| 216 |
+
|
| 217 |
+
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
|
| 218 |
+
outputs = model.generate(inputs, max_new_tokens=600, temperature=0.3, top_p=0.9)
|
| 219 |
+
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
| 220 |
+
```
|
| 221 |
+
|
| 222 |
+
#### Customization
|
| 223 |
+
|
| 224 |
+
- Want answers in Arabic or English? Modify rule #9 in the prompt.
|
| 225 |
+
- Need JSON output for an app? Replace the `OUTPUT_FORMAT` section with your schema.
|
| 226 |
+
- Building a chatbot for lawyers (not citizens)? Remove rule #6 to allow tactical recommendations.
|
| 227 |
+
|
| 228 |
+
|
| 229 |
## Training details
|
| 230 |
|
| 231 |
| Attribute | Value |
|