Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 3,744 Bytes
6b9d0d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | import pytest
from utils import *
# We use a F16 MOE gguf as main model, and q4_0 as draft model
server = ServerPreset.stories15m_moe()
MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/tiny-llamas/resolve/main/stories15M-q4_0.gguf"
def create_server():
global server
server = ServerPreset.stories15m_moe()
# set default values
server.model_draft = download_file(MODEL_DRAFT_FILE_URL)
server.draft_min = 4
server.draft_max = 8
server.fa = "off"
@pytest.fixture(autouse=True)
def fixture_create_server():
return create_server()
def test_with_and_without_draft():
global server
server.model_draft = None # disable draft model
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
"n_predict": 16,
})
assert res.status_code == 200
content_no_draft = res.body["content"]
server.stop()
# create new server with draft model
create_server()
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
"n_predict": 16,
})
assert res.status_code == 200
content_draft = res.body["content"]
assert content_no_draft == content_draft
def test_different_draft_min_draft_max():
global server
test_values = [
(1, 2),
(1, 4),
(4, 8),
(4, 12),
(8, 16),
]
last_content = None
for draft_min, draft_max in test_values:
server.stop()
server.draft_min = draft_min
server.draft_max = draft_max
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
"n_predict": 16,
})
assert res.status_code == 200
if last_content is not None:
assert last_content == res.body["content"]
last_content = res.body["content"]
def test_slot_ctx_not_exceeded():
global server
server.n_ctx = 256
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "Hello " * 248,
"temperature": 0.0,
"top_k": 1,
"speculative.p_min": 0.0,
})
assert res.status_code == 200
assert len(res.body["content"]) > 0
def test_with_ctx_shift():
global server
server.n_ctx = 256
server.enable_ctx_shift = True
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "Hello " * 248,
"temperature": 0.0,
"top_k": 1,
"n_predict": 256,
"speculative.p_min": 0.0,
})
assert res.status_code == 200
assert len(res.body["content"]) > 0
assert res.body["tokens_predicted"] == 256
assert res.body["truncated"] == True
@pytest.mark.parametrize("n_slots,n_requests", [
(1, 2),
(2, 2),
])
def test_multi_requests_parallel(n_slots: int, n_requests: int):
global server
server.n_slots = n_slots
server.start()
tasks = []
for _ in range(n_requests):
tasks.append((server.make_request, ("POST", "/completion", {
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
})))
results = parallel_function_calls(tasks)
for res in results:
assert res.status_code == 200
assert match_regex("(wise|kind|owl|answer)+", res.body["content"])
|