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
| // fix problem with std::min and std::max | |
| //#define MTMD_AUDIO_DEBUG | |
| size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) { | |
| size_t n_tokens = 0; | |
| for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { | |
| auto chunk = mtmd_input_chunks_get(chunks, i); | |
| n_tokens += mtmd_input_chunk_get_n_tokens(chunk); | |
| } | |
| return n_tokens; | |
| } | |
| llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks) { | |
| llama_pos n_pos = 0; | |
| for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { | |
| auto chunk = mtmd_input_chunks_get(chunks, i); | |
| n_pos += mtmd_input_chunk_get_n_pos(chunk); | |
| } | |
| return n_pos; | |
| } | |
| // helper struct to make working with embd batch easier | |
| // note: this will be removed after llama_batch_ext refactoring | |
| struct decode_embd_batch { | |
| int n_pos_per_embd; | |
| int n_mmproj_embd; | |
| std::vector<llama_pos> pos; | |
| std::vector<llama_pos> pos_view; // used by mrope | |
| std::vector<int32_t> n_seq_id; | |
| std::vector<llama_seq_id> seq_id_0; | |
| std::vector<llama_seq_id *> seq_ids; | |
| std::vector<int8_t> logits; | |
| llama_batch batch; | |
| decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) { | |
| pos .resize(n_tokens * n_pos_per_embd); | |
| n_seq_id.resize(n_tokens); | |
| seq_ids .resize(n_tokens + 1); | |
| logits .resize(n_tokens); | |
| seq_id_0.resize(1); | |
| seq_ids [n_tokens] = nullptr; | |
| batch = { | |
| /*n_tokens =*/ n_tokens, | |
| /*tokens =*/ nullptr, | |
| /*embd =*/ embd, | |
| /*pos =*/ pos.data(), | |
| /*n_seq_id =*/ n_seq_id.data(), | |
| /*seq_id =*/ seq_ids.data(), | |
| /*logits =*/ logits.data(), | |
| }; | |
| } | |
| void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) { | |
| seq_id_0[0] = seq_id; | |
| for (int i = 0; i < batch.n_tokens; i++) { | |
| batch.pos [i] = pos_0 + i; | |
| batch.n_seq_id[i] = 1; | |
| batch.seq_id [i] = seq_id_0.data(); | |
| batch.logits [i] = false; | |
| } | |
| } | |
| // M-RoPE for image | |
| void set_position_mrope_2d(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) { | |
| GGML_ASSERT(n_pos_per_embd == 4); | |
| seq_id_0[0] = seq_id; | |
| for (int y = 0; y < ny; y++) { | |
| for (int x = 0; x < nx; x++) { | |
| int i = y * nx + x; | |
| pos[i ] = pos_0; | |
| pos[i + batch.n_tokens ] = pos_0 + y; | |
| pos[i + batch.n_tokens * 2] = pos_0 + x; | |
| pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused | |
| } | |
| } | |
| for (int i = 0; i < batch.n_tokens; i++) { | |
| batch.n_seq_id[i] = 1; | |
| batch.seq_id [i] = seq_id_0.data(); | |
| batch.logits [i] = false; | |
| } | |
| } | |
| // M-RoPE for audio | |
| void set_position_mrope_1d(llama_pos pos_0, llama_seq_id seq_id) { | |
| GGML_ASSERT(n_pos_per_embd == 4); | |
| seq_id_0[0] = seq_id; | |
| for (int i = 0; i < batch.n_tokens; i++) { | |
| pos[i ] = pos_0 + i; | |
| pos[i + batch.n_tokens ] = pos_0 + i; | |
| pos[i + batch.n_tokens * 2] = pos_0 + i; | |
| pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused | |
| } | |
| for (int i = 0; i < batch.n_tokens; i++) { | |
| batch.n_seq_id[i] = 1; | |
| batch.seq_id [i] = seq_id_0.data(); | |
| batch.logits [i] = false; | |
| } | |
| } | |
| llama_batch get_view(int offset, int n_tokens) { | |
| llama_pos * pos_ptr; | |
| pos_view.clear(); | |
| pos_view.reserve(n_tokens * n_pos_per_embd); | |
| if (n_pos_per_embd > 1) { | |
| // mrope | |
| // for example, with layout of src: 1234...1234...1234...1234... | |
| // offset 2 will give us dst: 34...34...34...34... | |
| for (int i = 0; i < n_pos_per_embd; i++) { | |
| // assume n_tokens is less than or equal to batch.n_tokens | |
| // batch.n_tokens is number of **total** tokens | |
| // n_tokens is number of viewed token | |
| size_t src_idx = i * batch.n_tokens + offset; | |
| pos_view.insert(pos_view.end(), | |
| pos.data() + src_idx, | |
| pos.data() + src_idx + n_tokens); | |
| } | |
| pos_ptr = pos_view.data(); | |
| } else { | |
| // normal | |
| pos_ptr = pos.data() + offset; | |
| } | |
| return { | |
| /*n_tokens =*/ n_tokens, | |
| /*tokens =*/ nullptr, | |
| /*embd =*/ batch.embd + offset * n_mmproj_embd, | |
| /*pos =*/ pos_ptr, | |
| /*n_seq_id =*/ batch.n_seq_id + offset, | |
| /*seq_id =*/ batch.seq_id + offset, | |
| /*logits =*/ batch.logits + offset, | |
| }; | |
| } | |
| }; | |
| // Helper function for decoding an image whose embeddings have already been calculated | |
| int32_t mtmd_helper_decode_image_chunk( | |
| mtmd_context * ctx, | |
| struct llama_context * lctx, | |
| const mtmd_input_chunk * chunk, | |
| float * encoded_embd, | |
| llama_pos n_past, | |
| llama_seq_id seq_id, | |
| int32_t n_batch, | |
| llama_pos * new_n_past) { | |
| auto chunk_type = mtmd_input_chunk_get_type(chunk); | |
| const char * name = chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio"; | |
| if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { | |
| LOG_ERR("failed to decode chunk: input chunk not of image/audio type\n"); | |
| return -1; | |
| } | |
| const llama_model * model = llama_get_model(lctx); | |
| int n_mmproj_embd = llama_model_n_embd(model); | |
| int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1; | |
| int32_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk); | |
| int32_t i_batch = 0; | |
| int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; | |
| decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd); | |
| if (mtmd_decode_use_mrope(ctx)) { | |
| if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { | |
| const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); | |
| if (!image_tokens) { | |
| LOG_ERR("failed to decode chunk: image tokens are null\n"); | |
| return -1; | |
| } | |
| const int nx = mtmd_image_tokens_get_nx(image_tokens); | |
| const int ny = mtmd_image_tokens_get_ny(image_tokens); | |
| batch_embd.set_position_mrope_2d(n_past, nx, ny, seq_id); | |
| } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_AUDIO) { | |
| batch_embd.set_position_mrope_1d(n_past, seq_id); | |
| } else { | |
| GGML_ABORT("invalid chunk type for M-RoPE"); | |
| } | |
| } else { | |
| batch_embd.set_position_normal(n_past, seq_id); | |
| } | |
| if (mtmd_decode_use_non_causal(ctx)) { | |
| llama_set_causal_attn(lctx, false); | |
| // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image | |
| } | |
| while (i_batch < n_img_batches) { // split into batches | |
| int pos_offset = i_batch*n_batch; | |
| int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); | |
| llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); | |
| LOG_INF("decoding %s batch %d/%d, n_tokens_batch = %d\n", name, i_batch+1, n_img_batches, n_tokens_batch); | |
| int64_t t1 = ggml_time_ms(); | |
| int32_t ret = llama_decode(lctx, batch_embd_view); | |
| if (ret != 0) { | |
| LOG_ERR("failed to decode %s\n", name); | |
| llama_set_causal_attn(lctx, true); // restore causal attn | |
| return ret; | |
| } | |
| LOG_INF("%s decoded (batch %d/%d) in %" PRId64 " ms\n", name, i_batch+1, n_img_batches, ggml_time_ms() - t1); | |
| i_batch++; | |
| } | |
| n_past += mtmd_input_chunk_get_n_pos(chunk); | |
| *new_n_past = n_past; | |
| if (mtmd_decode_use_non_causal(ctx)) { | |
| llama_set_causal_attn(lctx, true); | |
| } | |
| return 0; | |
| } | |
| int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, | |
| struct llama_context * lctx, | |
| const mtmd_input_chunk * chunk, | |
| llama_pos n_past, | |
| llama_seq_id seq_id, | |
| int32_t n_batch, | |
| bool logits_last, | |
| llama_pos * new_n_past) { | |
| int32_t ret; | |
| llama_batch text_batch = llama_batch_init(n_batch, 0, 1); | |
| auto chunk_type = mtmd_input_chunk_get_type(chunk); | |
| if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { | |
| size_t n_tokens; | |
| const auto tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); | |
| // LOG_INF("decoding text chunk, n_tokens = %zu\n", n_tokens); | |
| size_t i = 0; | |
| while (i < n_tokens) { // split into batches | |
| text_batch.n_tokens = 0; // clear the batch | |
| for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) { | |
| int32_t j = text_batch.n_tokens; | |
| text_batch.token [j] = tokens[i]; | |
| text_batch.pos [j] = n_past++; | |
| text_batch.n_seq_id[j] = 1; | |
| text_batch.seq_id [j][0] = seq_id; | |
| text_batch.logits [j] = false; | |
| text_batch.n_tokens++; | |
| } | |
| bool is_last_token = (i == n_tokens); | |
| if (logits_last && is_last_token) { | |
| text_batch.logits[text_batch.n_tokens - 1] = true; | |
| } | |
| ret = llama_decode(lctx, text_batch); | |
| if (ret != 0) { | |
| LOG_ERR("failed to decode text\n"); | |
| llama_batch_free(text_batch); | |
| return ret; | |
| } | |
| *new_n_past += text_batch.n_tokens; | |
| } | |
| } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE || chunk_type == MTMD_INPUT_CHUNK_TYPE_AUDIO) { | |
| const char * name = chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio"; | |
| int64_t t0 = ggml_time_ms(); | |
| LOG_INF("encoding %s slice...\n", name); | |
| ret = mtmd_encode_chunk(ctx, chunk); | |
| if (ret != 0) { | |
| LOG_ERR("failed to encode %s slice\n", name); | |
| llama_batch_free(text_batch); | |
| return ret; | |
| } | |
| LOG_INF("%s slice encoded in %" PRId64 " ms\n", name, ggml_time_ms() - t0); | |
| float * embd = mtmd_get_output_embd(ctx); | |
| ret = mtmd_helper_decode_image_chunk(ctx, lctx, chunk, embd, n_past, seq_id, n_batch, new_n_past); | |
| if (ret != 0) { | |
| LOG_ERR("failed to decode %s\n", name); | |
| llama_batch_free(text_batch); | |
| return ret; | |
| } | |
| } else { | |
| GGML_ABORT("chunk type not supported"); | |
| } | |
| llama_batch_free(text_batch); | |
| return 0; | |
| } | |
| int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, | |
| struct llama_context * lctx, | |
| const mtmd_input_chunks * chunks, | |
| llama_pos n_past, | |
| llama_seq_id seq_id, | |
| int32_t n_batch, | |
| bool logits_last, | |
| llama_pos * new_n_past) { | |
| size_t n_chunks = mtmd_input_chunks_size(chunks); | |
| if (n_chunks == 0) { | |
| LOG_ERR("no chunks to eval\n"); | |
| return 0; | |
| } | |
| for (size_t i = 0; i < n_chunks; i++) { | |
| bool chunk_logits_last = (i == n_chunks - 1) && logits_last; | |
| auto chunk = mtmd_input_chunks_get(chunks, i); | |
| int32_t res = mtmd_helper_eval_chunk_single(ctx, lctx, chunk, n_past, seq_id, n_batch, chunk_logits_last, &n_past); | |
| if (res != 0) { | |
| LOG_ERR("failed to eval chunk %zu\n", i); | |
| return res; | |
| } | |
| *new_n_past = n_past; | |
| } | |
| return 0; | |
| } | |
| namespace audio_helpers { | |
| static bool is_audio_file(const char * buf, size_t len) { | |
| if (len < 12) { | |
| return false; | |
| } | |
| // RIFF ref: https://en.wikipedia.org/wiki/Resource_Interchange_File_Format | |
| // WAV ref: https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html | |
| bool is_wav = memcmp(buf, "RIFF", 4) == 0 && memcmp(buf + 8, "WAVE", 4) == 0; | |
| bool is_mp3 = len >= 3 && ( | |
| memcmp(buf, "ID3", 3) == 0 || | |
| // Check for MPEG sync word (simplified check) | |
| ((unsigned char)buf[0] == 0xFF && ((unsigned char)buf[1] & 0xE0) == 0xE0) | |
| ); | |
| bool is_flac = memcmp(buf, "fLaC", 4) == 0; | |
| return is_wav || is_mp3 || is_flac; | |
| } | |
| // returns true if the buffer is a valid audio file | |
| static bool decode_audio_from_buf(const unsigned char * buf_in, size_t len, int target_sampler_rate, std::vector<float> & pcmf32_mono) { | |
| ma_result result; | |
| const int channels = 1; | |
| ma_decoder_config decoder_config = ma_decoder_config_init(ma_format_f32, channels, target_sampler_rate); | |
| ma_decoder decoder; | |
| result = ma_decoder_init_memory(buf_in, len, &decoder_config, &decoder); | |
| if (result != MA_SUCCESS) { | |
| return false; | |
| } | |
| ma_uint64 frame_count; | |
| ma_uint64 frames_read; | |
| result = ma_decoder_get_length_in_pcm_frames(&decoder, &frame_count); | |
| if (result != MA_SUCCESS) { | |
| ma_decoder_uninit(&decoder); | |
| return false; | |
| } | |
| pcmf32_mono.resize(frame_count); | |
| result = ma_decoder_read_pcm_frames(&decoder, pcmf32_mono.data(), frame_count, &frames_read); | |
| if (result != MA_SUCCESS) { | |
| ma_decoder_uninit(&decoder); | |
| return false; | |
| } | |
| // save audio to wav file | |
| ma_encoder_config config = ma_encoder_config_init(ma_encoding_format_wav, ma_format_f32, 1, target_sampler_rate); | |
| ma_encoder encoder; | |
| ma_encoder_init_file("output.wav", &config, &encoder); | |
| ma_encoder_write_pcm_frames(&encoder, pcmf32_mono.data(), pcmf32_mono.size(), &frames_read); | |
| ma_encoder_uninit(&encoder); | |
| ma_decoder_uninit(&decoder); | |
| return true; | |
| } | |
| } // namespace audio_helpers | |
| mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len) { | |
| if (audio_helpers::is_audio_file((const char *)buf, len)) { | |
| std::vector<float> pcmf32; | |
| int bitrate = mtmd_get_audio_bitrate(ctx); | |
| if (bitrate < 0) { | |
| LOG_ERR("This model does not support audio input\n"); | |
| return nullptr; | |
| } | |
| if (!audio_helpers::decode_audio_from_buf(buf, len, bitrate, pcmf32)) { | |
| LOG_ERR("Unable to read WAV audio file from buffer\n"); | |
| return nullptr; | |
| } | |
| return mtmd_bitmap_init_from_audio(pcmf32.size(), pcmf32.data()); | |
| } | |
| // otherwise, we assume it's an image | |
| mtmd_bitmap * result = nullptr; | |
| { | |
| int nx, ny, nc; | |
| auto * data = stbi_load_from_memory(buf, len, &nx, &ny, &nc, 3); | |
| if (!data) { | |
| LOG_ERR("%s: failed to decode image bytes\n", __func__); | |
| return nullptr; | |
| } | |
| result = mtmd_bitmap_init(nx, ny, data); | |
| stbi_image_free(data); | |
| } | |
| return result; | |
| } | |
| mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname) { | |
| std::vector<unsigned char> buf; | |
| FILE * f = fopen(fname, "rb"); | |
| if (!f) { | |
| LOG_ERR("Unable to open file %s: %s\n", fname, strerror(errno)); | |
| return nullptr; | |
| } | |
| fseek(f, 0, SEEK_END); | |
| long file_size = ftell(f); | |
| fseek(f, 0, SEEK_SET); | |
| buf.resize(file_size); | |
| size_t n_read = fread(buf.data(), 1, file_size, f); | |
| fclose(f); | |
| if (n_read != (size_t)file_size) { | |
| LOG_ERR("Failed to read entire file %s", fname); | |
| return nullptr; | |
| } | |
| return mtmd_helper_bitmap_init_from_buf(ctx, buf.data(), buf.size()); | |
| } | |