Text Generation
GGUF
Japanese
English
llama.cpp
Mixture of Experts
expert-pruning
intel-mac
cpu
local-agent
imatrix
conversational
Instructions to use miutti/intel-mac-local-llm 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 miutti/intel-mac-local-llm 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 miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
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 miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
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 miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- LM Studio
- Jan
- vLLM
How to use miutti/intel-mac-local-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miutti/intel-mac-local-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miutti/intel-mac-local-llm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Ollama
How to use miutti/intel-mac-local-llm with Ollama:
ollama run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Unsloth Desktop
- Pi
How to use miutti/intel-mac-local-llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
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": "miutti/intel-mac-local-llm:UD-Q2_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use miutti/intel-mac-local-llm with Docker Model Runner:
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Lemonade
How to use miutti/intel-mac-local-llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miutti/intel-mac-local-llm:UD-Q2_K_XL
Run and chat with the model
lemonade run user.intel-mac-local-llm-UD-Q2_K_XL
List all available models
lemonade list
- Hermes Agent
How to use miutti/intel-mac-local-llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
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 miutti/intel-mac-local-llm:UD-Q2_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use miutti/intel-mac-local-llm with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
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 "miutti/intel-mac-local-llm:UD-Q2_K_XL" \ --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"
Download source/kernel/tests/test_public_bench.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 11 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_public_bench.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/tests/test_public_bench.py
-
curl -L -o test_public_bench.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_public_bench.py
11 kB
| #!/usr/bin/env python3 | |
| import io | |
| import json | |
| import sys | |
| import tempfile | |
| import unittest | |
| import urllib.error | |
| from pathlib import Path | |
| from unittest import mock | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[1])) | |
| import public_bench as pb | |
| class Response: | |
| def __init__(self, status, body): | |
| self.status = status | |
| self.body = body | |
| def __enter__(self): | |
| return self | |
| def __exit__(self, *args): | |
| return False | |
| def getcode(self): | |
| return self.status | |
| def read(self): | |
| return self.body | |
| def success_result(content="A", error=None): | |
| return { | |
| "content": content, "reasoning_chars": 0, "elapsed_sec": 0.01, | |
| "timings": {}, "usage": None, "model": "test", "error": error, | |
| "error_type": "test" if error else None, "http_status": 500 if error else 200, | |
| "error_body": "failure" if error else "", "attempts": [], "retry_count": 0, | |
| "prompt_hash": "hash", | |
| } | |
| class PublicBenchTests(unittest.TestCase): | |
| def test_balanced_sample_is_exact_and_stable(self): | |
| rows = [{"id": str(i), "domain": domain} | |
| for i, domain in enumerate("aaabbbcccc")] | |
| first = pb.balanced_sample(rows, "domain", 7, 123, | |
| lambda row: row["id"]) | |
| second = pb.balanced_sample(list(reversed(rows)), "domain", 7, 123, | |
| lambda row: row["id"]) | |
| self.assertEqual([x["id"] for x in first], [x["id"] for x in second]) | |
| self.assertEqual(len(first), 7) | |
| counts = {domain: sum(x["domain"] == domain for x in first) | |
| for domain in "abc"} | |
| self.assertEqual(sum(counts.values()), 7) | |
| self.assertLessEqual(max(counts.values()) - min(counts.values()), 1) | |
| def test_client_retries_and_records_http_body(self): | |
| calls = [] | |
| def opener(request, timeout): | |
| calls.append(request) | |
| if len(calls) == 1: | |
| raise urllib.error.HTTPError( | |
| request.full_url, 500, "server", {}, io.BytesIO(b"overloaded")) | |
| return Response(200, b'{"choices":[{"message":{"content":"A"}}]}') | |
| client = pb.LocalClient("http://example.test/v1/chat/completions", False, | |
| 8, 1, retries=2, retry_backoff=0, opener=opener) | |
| result = client.call("question", "item-1") | |
| self.assertEqual(result["content"], "A") | |
| self.assertEqual(len(calls), 2) | |
| self.assertEqual(result["retry_count"], 1) | |
| self.assertEqual(result["attempts"][0]["status"], 500) | |
| self.assertEqual(result["attempts"][0]["body_excerpt"], "overloaded") | |
| self.assertIsNone(result["error"]) | |
| def test_client_does_not_repeat_reasoning_only_truncation(self): | |
| calls = [] | |
| def opener(request, timeout): | |
| calls.append(request) | |
| return Response( | |
| 200, | |
| b'{"choices":[{"finish_reason":"length","message":' | |
| b'{"content":"","reasoning_content":"long thought"}}]}', | |
| ) | |
| client = pb.LocalClient("http://example.test/v1/chat/completions", True, | |
| 8, 1, retries=3, retry_backoff=0, opener=opener) | |
| result = client.call("question", "item-1") | |
| self.assertEqual(len(calls), 1) | |
| self.assertEqual(result["error_type"], "OutputTruncatedError") | |
| self.assertEqual(result["finish_reason"], "length") | |
| self.assertEqual(result["reasoning_chars"], len("long thought")) | |
| def test_adaptive_client_votes_and_assigns_max_profile_to_aime(self): | |
| class FakeClient: | |
| seed = 100 | |
| def __init__(self): | |
| self.calls = [] | |
| def call(self, prompt, item_id, **kwargs): | |
| self.calls.append((prompt, item_id, kwargs)) | |
| answers = {"sample-1": "7", "sample-2": "8", "sample-3": "7"} | |
| content = next((value for key, value in answers.items() | |
| if key in item_id), "7") | |
| return { | |
| "content": content, "error": None, "error_type": None, | |
| "http_status": 200, "error_body": "", "attempts": [], | |
| "retry_count": 0, "elapsed_sec": 0.01, "timings": {}, | |
| "usage": None, "model": "test", "reasoning_chars": 10, | |
| "request": kwargs, | |
| } | |
| profiles = { | |
| "fixed": pb.RequestProfile("fixed", False, 8, 1), | |
| "fast": pb.RequestProfile("fast", False, 8, 1), | |
| "deep": pb.RequestProfile("deep", True, 16, 2), | |
| "max": pb.RequestProfile("max", True, 32, 3), | |
| } | |
| fake = FakeClient() | |
| client = pb.AdaptiveClient( | |
| fake, "aime", "adaptive", profiles, pb.extract_integer, | |
| samples=3, sample_temperature=0.35, | |
| ) | |
| result = client.call("Solve this problem", "aime/1") | |
| self.assertEqual(result["winner"], "7") | |
| self.assertEqual(result["vote_counts"], {"7": 2, "8": 1}) | |
| self.assertEqual(result["profile"], "max") | |
| self.assertEqual(result["request_count"], 3) | |
| self.assertTrue(all(call[2]["think"] for call in fake.calls)) | |
| self.assertTrue(all(call[2]["max_tokens"] == 32 for call in fake.calls)) | |
| def test_adaptive_client_retries_fast_when_answer_format_is_invalid(self): | |
| class FakeClient: | |
| seed = 0 | |
| def __init__(self): | |
| self.calls = [] | |
| def call(self, prompt, item_id, **kwargs): | |
| self.calls.append((item_id, kwargs)) | |
| content = "not an answer" if "fallback" not in item_id else "B" | |
| return { | |
| "content": content, "error": None, "error_type": None, | |
| "http_status": 200, "error_body": "", "attempts": [], | |
| "retry_count": 0, "elapsed_sec": 0.01, "timings": {}, | |
| "usage": None, "model": "test", "reasoning_chars": 0, | |
| "request": kwargs, | |
| } | |
| profiles = { | |
| "fixed": pb.RequestProfile("fixed", False, 8, 1), | |
| "fast": pb.RequestProfile("fast", False, 8, 1), | |
| "deep": pb.RequestProfile("deep", True, 16, 2), | |
| } | |
| profiles["max"] = pb.RequestProfile("max", True, 32, 3) | |
| fake = FakeClient() | |
| client = pb.AdaptiveClient( | |
| fake, "mmlu_pro", "adaptive", profiles, | |
| lambda text: pb.extract_letter(text, "ABCD"), | |
| ) | |
| result = client.call("Choose one", "mmlu/1") | |
| self.assertEqual(result["content"], "B") | |
| self.assertEqual(len(fake.calls), 2) | |
| self.assertEqual(fake.calls[-1][1]["think"], False) | |
| self.assertEqual(fake.calls[-1][1]["max_tokens"], 264) | |
| def test_max_client_rescues_reasoning_only_response_with_fast_call(self): | |
| class FakeClient: | |
| seed = 0 | |
| def __init__(self): | |
| self.calls = [] | |
| def call(self, prompt, item_id, **kwargs): | |
| self.calls.append((item_id, kwargs)) | |
| if "fallback-fast" not in item_id: | |
| return { | |
| "item_id": item_id, "content": "", "error": "cut", | |
| "error_type": "OutputTruncatedError", "elapsed_sec": 1, | |
| "timings": {}, "request": kwargs, | |
| } | |
| return { | |
| "item_id": item_id, "content": "C", "error": None, | |
| "error_type": None, "elapsed_sec": 0.1, "timings": {}, | |
| "request": kwargs, | |
| } | |
| profiles = { | |
| "fixed": pb.RequestProfile("fixed", False, 8, 1), | |
| "fast": pb.RequestProfile("fast", False, 8, 1), | |
| "deep": pb.RequestProfile("deep", True, 16, 2), | |
| "max": pb.RequestProfile("max", True, 32, 3), | |
| } | |
| fake = FakeClient() | |
| client = pb.AdaptiveClient( | |
| fake, "gpqa_diamond", "max", profiles, | |
| lambda text: pb.extract_letter(text, "ABCD"), | |
| ) | |
| result = client.call("Choose one", "gpqa/1") | |
| self.assertEqual(result["content"], "C") | |
| self.assertTrue(result["fallback_used"]) | |
| self.assertEqual([kwargs["think"] for _, kwargs in fake.calls], [True, False]) | |
| def test_corrupt_cache_is_quarantined_and_refetched(self): | |
| with tempfile.TemporaryDirectory() as directory: | |
| out = Path(directory) | |
| cache = out / "datasets" / "x__c__test.jsonl" | |
| cache.parent.mkdir() | |
| cache.write_text('{broken\n', encoding="utf-8") | |
| with mock.patch.object(pb, "get_json", return_value={ | |
| "num_rows_total": 1, "rows": [{"row": {"id": 1}}] | |
| }): | |
| rows = pb.fetch_rows("x", "c", "test", out) | |
| self.assertEqual(rows, [{"id": 1}]) | |
| self.assertTrue(list(cache.parent.glob(cache.name + ".corrupt-*"))) | |
| self.assertEqual(pb.read_jsonl(cache), [{"id": 1}]) | |
| def test_run_rows_retries_failed_existing_item_only(self): | |
| class FakeClient: | |
| def __init__(self, result): | |
| self.result = result | |
| self.calls = [] | |
| def call(self, prompt, item_id): | |
| self.calls.append(item_id) | |
| return dict(self.result) | |
| rows = [{"id": "one", "answer": "A"}, {"id": "two", "answer": "A"}] | |
| with tempfile.TemporaryDirectory() as directory: | |
| out = Path(directory) | |
| first_client = FakeClient(success_result("", "HTTPError")) | |
| first = pb.run_rows("toy", rows, lambda row: row["id"], | |
| lambda content: content or None, first_client, out, | |
| lambda row: row["id"], gold_fn=lambda row: "A") | |
| self.assertEqual(first["failed_n"], 2) | |
| self.assertEqual(first_client.calls, ["one", "two"]) | |
| second_client = FakeClient(success_result("A")) | |
| second = pb.run_rows("toy", rows, lambda row: row["id"], | |
| lambda content: content, second_client, out, | |
| lambda row: row["id"], gold_fn=lambda row: "A") | |
| self.assertEqual(second["failed_n"], 0) | |
| self.assertEqual(second["correct"], 2) | |
| self.assertEqual(second_client.calls, ["one", "two"]) | |
| items = pb.read_jsonl(out / "toy.jsonl") | |
| self.assertEqual(len(items), 2) | |
| self.assertTrue(all(item["row_sha256"] for item in items)) | |
| def test_atomic_jsonl_is_valid_after_write(self): | |
| with tempfile.TemporaryDirectory() as directory: | |
| path = Path(directory) / "rows.jsonl" | |
| pb.jsonl_dump(path, [{"x": 1}, {"x": 2}]) | |
| self.assertEqual(pb.read_jsonl(path), [{"x": 1}, {"x": 2}]) | |
| self.assertFalse(list(Path(directory).glob("*.tmp"))) | |
| if __name__ == "__main__": | |
| unittest.main() | |