Instructions to use AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
Use Docker
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AJKADZ/PHI_CODER with Ollama:
ollama run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AJKADZ/PHI_CODER with Docker Model Runner:
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Lemonade
How to use AJKADZ/PHI_CODER with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJKADZ/PHI_CODER:Q4_K_M
Run and chat with the model
lemonade run user.PHI_CODER-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 2,268 Bytes
9b19fd8 | 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 | #include <stdio.h>
#include <assert.h>
#include "mtmd.h"
int main(void) {
printf("\n\nTesting libmtmd C API...\n");
printf("--------\n\n");
struct mtmd_context_params params = mtmd_context_params_default();
printf("Default image marker: %s\n", params.image_marker);
mtmd_input_chunks * chunks = mtmd_test_create_input_chunks();
if (!chunks) {
fprintf(stderr, "Failed to create input chunks\n");
return 1;
}
size_t n_chunks = mtmd_input_chunks_size(chunks);
printf("Number of chunks: %zu\n", n_chunks);
assert(n_chunks > 0);
for (size_t i = 0; i < n_chunks; i++) {
const mtmd_input_chunk * chunk = mtmd_input_chunks_get(chunks, i);
assert(chunk != NULL);
enum mtmd_input_chunk_type type = mtmd_input_chunk_get_type(chunk);
printf("Chunk %zu type: %d\n", i, type);
if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
size_t n_tokens;
const llama_token * tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens);
printf(" Text chunk with %zu tokens\n", n_tokens);
assert(tokens != NULL);
assert(n_tokens > 0);
for (size_t j = 0; j < n_tokens; j++) {
assert(tokens[j] >= 0);
printf(" > Token %zu: %d\n", j, tokens[j]);
}
} else if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
const mtmd_image_tokens * image_tokens = mtmd_input_chunk_get_tokens_image(chunk);
size_t n_tokens = mtmd_image_tokens_get_n_tokens(image_tokens);
size_t nx = mtmd_image_tokens_get_nx(image_tokens);
size_t ny = mtmd_image_tokens_get_ny(image_tokens);
const char * id = mtmd_image_tokens_get_id(image_tokens);
assert(n_tokens > 0);
assert(nx > 0);
assert(ny > 0);
assert(id != NULL);
printf(" Image chunk with %zu tokens\n", n_tokens);
printf(" Image size: %zu x %zu\n", nx, ny);
printf(" Image ID: %s\n", id);
}
}
// Free the chunks
mtmd_input_chunks_free(chunks);
printf("\n\nDONE: test libmtmd C API...\n");
return 0;
}
|