Instructions to use ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx 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("ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx") 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
- MLX LM
How to use ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ivanfioravanti/V4F-Q4KExperts-Q8Rest-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|begin▁of▁sentence|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|end▁of▁sentence|>", | |
| "is_local": true, | |
| "legacy": true, | |
| "local_files_only": false, | |
| "model_max_length": 1048576, | |
| "pad_token": "<|end▁of▁sentence|>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": null, | |
| "chat_template": "{{- '<|begin▁of▁sentence|>' -}}\n{%- if enable_thinking and reasoning_effort is defined and reasoning_effort == 'max' -%}\n{{- 'Reasoning Effort: Absolute maximum with no shortcuts permitted.\nYou MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\nExplicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n' -}}\n{%- endif -%}\n{%- if messages[0]['role'] == 'system' -%}\n{{- messages[0]['content'] -}}\n{%- set start = 1 -%}\n{%- else -%}\n{%- set start = 0 -%}\n{%- endif -%}\n{%- for m in messages[start:] -%}\n{%- if m['role'] == 'user' -%}\n{{- '<|User|>' + m['content'] -}}\n{%- elif m['role'] == 'assistant' -%}\n{{- '<|Assistant|>' -}}\n{%- if enable_thinking and m.get('reasoning_content') -%}\n{{- '<think>' + m['reasoning_content'] + '</think>' -}}\n{%- else -%}\n{{- '</think>' -}}\n{%- endif -%}\n{{- m['content'] + '<|end▁of▁sentence|>' -}}\n{%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n{{- '<|Assistant|>' -}}\n{%- if enable_thinking -%}\n{{- '<think>' -}}\n{%- else -%}\n{{- '</think>' -}}\n{%- endif -%}\n{%- endif -%}" | |
| } | |