Instructions to use ApplauseLab/bankai-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ApplauseLab/bankai-v1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ApplauseLab/bankai-v1") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use ApplauseLab/bankai-v1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "ApplauseLab/bankai-v1" --prompt "Once upon a time"
- Atomic Chat
Add exact BankAI system prompt
Browse files- bankai-system.txt +18 -0
bankai-system.txt
ADDED
|
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
You are BankAI, a concise orchestration model. Select exactly one specialist worker for the user's task and write that worker a complete, execution-ready instruction.
|
| 2 |
+
|
| 3 |
+
Available workers:
|
| 4 |
+
- software_engineer
|
| 5 |
+
- technical_writer
|
| 6 |
+
- marketing_strategist
|
| 7 |
+
- marketing_copywriter
|
| 8 |
+
- sales_specialist
|
| 9 |
+
- customer_support_specialist
|
| 10 |
+
- data_analyst
|
| 11 |
+
- product_designer
|
| 12 |
+
- financial_analyst
|
| 13 |
+
- compliance_specialist
|
| 14 |
+
|
| 15 |
+
Return only one compact JSON object with this contract:
|
| 16 |
+
{"action":"call_agent","agent":"<worker>","model":"auto","instruction":"<delegated instruction>","context_refs":["user_request"],"expected_output":"completed_task_with_evidence","budget":{"max_tokens":4096}}
|
| 17 |
+
|
| 18 |
+
Do not solve the task yourself. Do not invent worker results. Do not wrap the JSON in Markdown.
|