Instructions to use nativ-community/flint-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/flint-bf16 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("nativ-community/flint-bf16") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use nativ-community/flint-bf16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nativ-community/flint-bf16"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "nativ-community/flint-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use nativ-community/flint-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "nativ-community/flint-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "nativ-community/flint-bf16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nativ-community/flint-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use nativ-community/flint-bf16 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nativ-community/flint-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 nativ-community/flint-bf16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nativ-community/flint-bf16 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nativ-community/flint-bf16"
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 "nativ-community/flint-bf16" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download validation.json from nativ-community/flint-bf16: direct link, hf CLI and curl.
- Browser
- Download file 3.23 kB
-
https://huggingface.co/nativ-community/flint-bf16/resolve/main/validation.json
- Command line
-
hf download hf://nativ-community/flint-bf16/validation.json
-
curl -L -o validation.json https://huggingface.co/nativ-community/flint-bf16/resolve/main/validation.json
3.23 kB
| { | |
| "format": "bf16", | |
| "strict_reload": true, | |
| "finite_prefill_decode_logits": true, | |
| "cache_offsets_correct": true, | |
| "source_sidecars_identical": true, | |
| "native_thinking_template_preserved": true, | |
| "temperature": 0.0, | |
| "max_tokens": 512, | |
| "smoke_tests": [ | |
| { | |
| "task": "arithmetic", | |
| "prompt": "What is 17 multiplied by 23? Give the answer.", | |
| "text": "<think>We need answer simple multiplication. Need maybe just answer. 17*23=391. Ensure no extra? User asks \"What is 17 multiplied by 23? Give the answer.\" So final maybe 391.</think>391", | |
| "answer": "391", | |
| "checks": { | |
| "expected_391": true | |
| }, | |
| "generated_tokens": 48, | |
| "seconds": 3.0285172078292817 | |
| }, | |
| { | |
| "task": "retrieval", | |
| "prompt": "The passcode is 941731. Repeat the passcode exactly.", | |
| "text": "<think>The user is asking me to repeat a passcode. This is a simple request. I should just repeat it exactly as given.\n\nThe passcode is 941731.\n\nI should repeat it exactly.</think>941731", | |
| "answer": "941731", | |
| "checks": { | |
| "expected_passcode": true | |
| }, | |
| "generated_tokens": 43, | |
| "seconds": 2.515628542052582 | |
| }, | |
| { | |
| "task": "code", | |
| "prompt": "Write a Python function add(a, b) that returns a + b.", | |
| "text": "<think>We need to write a Python function add(a, b) that returns a + b. This is straightforward. The user likely expects a simple implementation.\n\nWe should provide the code, maybe with a brief explanation.\n\nThe instruction: \"Write a Python function add(a, b) that returns a + b.\"\n\nSo we can just output:\n\ndef add(a, b):\n return a + b\n\nThat's it.\n\nBut maybe they want a full response with code block. I'll provide the function definition and maybe a quick test.\n\nSince it's a simple task, I'll just give the code.</think>Here's a simple Python function that adds two numbers:\n\n```python\ndef add(a, b):\n return a + b\n```\n\nYou can use it like this:\n\n```python\nresult = add(3, 5)\nprint(result) # Output: 8\n```", | |
| "answer": "Here's a simple Python function that adds two numbers:\n\n```python\ndef add(a, b):\n return a + b\n```\n\nYou can use it like this:\n\n```python\nresult = add(3, 5)\nprint(result) # Output: 8\n```", | |
| "checks": { | |
| "add_function": true | |
| }, | |
| "generated_tokens": 175, | |
| "seconds": 10.09182208403945 | |
| } | |
| ], | |
| "all_smoke_checks_pass": true, | |
| "device": { | |
| "device_name": "Apple M5 Max", | |
| "max_recommended_working_set_size": 115448725504, | |
| "memory_size": 137438953472, | |
| "architecture": "applegpu_g17s", | |
| "max_buffer_length": 86586540032, | |
| "resource_limit": 499000 | |
| }, | |
| "mlx_version": "0.32.4", | |
| "mlx_vlm_version": "0.7.10", | |
| "converter_revision": "6d9ba328abbd6129c7461eda86280aa9fbfd9986", | |
| "python": "3.12.15", | |
| "peak_active_gb": 80.835695544, | |
| "seconds": 41.7588444580324, | |
| "swap_before": "vm.swapusage: total = 10240.00M used = 8737.00M free = 1503.00M (encrypted)", | |
| "swap_after": "vm.swapusage: total = 15360.00M used = 14076.81M free = 1283.19M (encrypted)", | |
| "limits": "Three synthetic smoke prompts and finite/cache checks; not a quality benchmark or proof of parity to BF16/Transformers." | |
| } |