Instructions to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./llama-cli -hf Aliguinga01/rule_violation2:F16
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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16
Use Docker
docker model run hf.co/Aliguinga01/rule_violation2:F16
- LM Studio
- Jan
- Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
ollama run hf.co/Aliguinga01/rule_violation2:F16
- Unsloth Studio
How to use Aliguinga01/rule_violation2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aliguinga01/rule_violation2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
docker model run hf.co/Aliguinga01/rule_violation2:F16
- Lemonade
How to use Aliguinga01/rule_violation2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aliguinga01/rule_violation2:F16
Run and chat with the model
lemonade run user.rule_violation2-F16
List all available models
lemonade list
File size: 2,753 Bytes
4d35814 | 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 | #include "ggml.h"
#include "ggml-cpu.h"
#include <chrono>
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#include <vector>
#include <thread>
#define MAX_NARGS 2
int main(int argc, char *argv[]) {
int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
int n_rounds = 100;
if (argc > 1) {
n_threads = std::atoi(argv[1]);
}
if (argc > 2) {
n_rounds = std::atoi(argv[2]);
}
struct ggml_init_params params = {
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
// Create graph
struct ggml_cgraph * gf = ggml_new_graph(ctx);
// Lots of small, parallel ops where barriers in between will dominate
struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64);
for (int i = 0; i < 1000; i++) {
struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
out = ggml_mul_mat(ctx, a, out);
struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
out = ggml_mul_mat(ctx, d, out);
}
ggml_build_forward_expand(gf, out);
int n_nodes = ggml_graph_n_nodes(gf);
// Create threadpool
struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads);
struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
exit(1);
}
// Create compute plan
struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool);
std::vector<uint8_t> work_data(cplan.work_size);
cplan.work_data = work_data.data();
std::cerr << "graph-compute with"
<< "\n n_threads: " << n_threads
<< "\n n_nodes: " << n_nodes
<< "\n n_rounds: " << n_rounds
<< "\n";
// ggml_graph_print(gf);
// Warmup
ggml_graph_compute(gf, &cplan);
auto t0 = std::chrono::high_resolution_clock::now();
for (int i=0; i < n_rounds; i++) {
ggml_graph_compute(gf, &cplan);
}
auto t1 = std::chrono::high_resolution_clock::now();
auto usec = std::chrono::duration_cast<std::chrono::microseconds>(t1-t0).count();
auto nsec = std::chrono::duration_cast<std::chrono::nanoseconds>(t1-t0).count();
std::cerr << "graph-compute took " << usec << " usec "
<< "\n " << (float) usec / n_rounds << " usec per-iter"
<< "\n " << (float) nsec / (n_rounds * n_nodes) << " nsec per-node"
<< "\n";
ggml_threadpool_free(threadpool);
ggml_free(ctx);
return 0;
}
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