Text Generation
Transformers
Safetensors
GGUF
llama
chatbot
multilingual
arabic
french
tamazight
english
conversational
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use kaisser/LLM-Maroc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaisser/LLM-Maroc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kaisser/LLM-Maroc") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kaisser/LLM-Maroc") model = AutoModelForCausalLM.from_pretrained("kaisser/LLM-Maroc", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kaisser/LLM-Maroc 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 kaisser/LLM-Maroc:BF16 # Run inference directly in the terminal: llama cli -hf kaisser/LLM-Maroc:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kaisser/LLM-Maroc:BF16 # Run inference directly in the terminal: llama cli -hf kaisser/LLM-Maroc:BF16
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 kaisser/LLM-Maroc:BF16 # Run inference directly in the terminal: ./llama-cli -hf kaisser/LLM-Maroc:BF16
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 kaisser/LLM-Maroc:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kaisser/LLM-Maroc:BF16
Use Docker
docker model run hf.co/kaisser/LLM-Maroc:BF16
- LM Studio
- Jan
- vLLM
How to use kaisser/LLM-Maroc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kaisser/LLM-Maroc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaisser/LLM-Maroc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kaisser/LLM-Maroc:BF16
- SGLang
How to use kaisser/LLM-Maroc with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "kaisser/LLM-Maroc" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaisser/LLM-Maroc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "kaisser/LLM-Maroc" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaisser/LLM-Maroc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use kaisser/LLM-Maroc with Ollama:
ollama run hf.co/kaisser/LLM-Maroc:BF16
- Unsloth Desktop
- Docker Model Runner
How to use kaisser/LLM-Maroc with Docker Model Runner:
docker model run hf.co/kaisser/LLM-Maroc:BF16
- Lemonade
How to use kaisser/LLM-Maroc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kaisser/LLM-Maroc:BF16
Run and chat with the model
lemonade run user.LLM-Maroc-BF16
List all available models
lemonade list
- Atomic Chat
| // | |
| // gguf constants (sync with gguf.py) | |
| // | |
| enum llm_arch { | |
| LLM_ARCH_LLAMA, | |
| LLM_ARCH_LLAMA4, | |
| LLM_ARCH_DECI, | |
| LLM_ARCH_FALCON, | |
| LLM_ARCH_BAICHUAN, | |
| LLM_ARCH_GROK, | |
| LLM_ARCH_GPT2, | |
| LLM_ARCH_GPTJ, | |
| LLM_ARCH_GPTNEOX, | |
| LLM_ARCH_MPT, | |
| LLM_ARCH_STARCODER, | |
| LLM_ARCH_REFACT, | |
| LLM_ARCH_BERT, | |
| LLM_ARCH_NOMIC_BERT, | |
| LLM_ARCH_NOMIC_BERT_MOE, | |
| LLM_ARCH_NEO_BERT, | |
| LLM_ARCH_JINA_BERT_V2, | |
| LLM_ARCH_BLOOM, | |
| LLM_ARCH_STABLELM, | |
| LLM_ARCH_QWEN, | |
| LLM_ARCH_QWEN2, | |
| LLM_ARCH_QWEN2MOE, | |
| LLM_ARCH_QWEN2VL, | |
| LLM_ARCH_QWEN3, | |
| LLM_ARCH_QWEN3MOE, | |
| LLM_ARCH_PHI2, | |
| LLM_ARCH_PHI3, | |
| LLM_ARCH_PHIMOE, | |
| LLM_ARCH_PLAMO, | |
| LLM_ARCH_PLAMO2, | |
| LLM_ARCH_CODESHELL, | |
| LLM_ARCH_ORION, | |
| LLM_ARCH_INTERNLM2, | |
| LLM_ARCH_MINICPM, | |
| LLM_ARCH_MINICPM3, | |
| LLM_ARCH_GEMMA, | |
| LLM_ARCH_GEMMA2, | |
| LLM_ARCH_GEMMA3, | |
| LLM_ARCH_GEMMA3N, | |
| LLM_ARCH_STARCODER2, | |
| LLM_ARCH_MAMBA, | |
| LLM_ARCH_MAMBA2, | |
| LLM_ARCH_JAMBA, | |
| LLM_ARCH_FALCON_H1, | |
| LLM_ARCH_XVERSE, | |
| LLM_ARCH_COMMAND_R, | |
| LLM_ARCH_COHERE2, | |
| LLM_ARCH_DBRX, | |
| LLM_ARCH_OLMO, | |
| LLM_ARCH_OLMO2, | |
| LLM_ARCH_OLMOE, | |
| LLM_ARCH_OPENELM, | |
| LLM_ARCH_ARCTIC, | |
| LLM_ARCH_DEEPSEEK, | |
| LLM_ARCH_DEEPSEEK2, | |
| LLM_ARCH_CHATGLM, | |
| LLM_ARCH_GLM4, | |
| LLM_ARCH_BITNET, | |
| LLM_ARCH_T5, | |
| LLM_ARCH_T5ENCODER, | |
| LLM_ARCH_JAIS, | |
| LLM_ARCH_NEMOTRON, | |
| LLM_ARCH_EXAONE, | |
| LLM_ARCH_EXAONE4, | |
| LLM_ARCH_RWKV6, | |
| LLM_ARCH_RWKV6QWEN2, | |
| LLM_ARCH_RWKV7, | |
| LLM_ARCH_ARWKV7, | |
| LLM_ARCH_GRANITE, | |
| LLM_ARCH_GRANITE_MOE, | |
| LLM_ARCH_GRANITE_HYBRID, | |
| LLM_ARCH_CHAMELEON, | |
| LLM_ARCH_WAVTOKENIZER_DEC, | |
| LLM_ARCH_PLM, | |
| LLM_ARCH_BAILINGMOE, | |
| LLM_ARCH_DOTS1, | |
| LLM_ARCH_ARCEE, | |
| LLM_ARCH_ERNIE4_5, | |
| LLM_ARCH_ERNIE4_5_MOE, | |
| LLM_ARCH_HUNYUAN_MOE, | |
| LLM_ARCH_SMOLLM3, | |
| LLM_ARCH_LFM2, | |
| LLM_ARCH_DREAM, | |
| LLM_ARCH_UNKNOWN, | |
| }; | |
| enum llm_kv { | |
| LLM_KV_GENERAL_TYPE, | |
| LLM_KV_GENERAL_ARCHITECTURE, | |
| LLM_KV_GENERAL_QUANTIZATION_VERSION, | |
| LLM_KV_GENERAL_ALIGNMENT, | |
| LLM_KV_GENERAL_FILE_TYPE, | |
| LLM_KV_GENERAL_NAME, | |
| LLM_KV_GENERAL_AUTHOR, | |
| LLM_KV_GENERAL_VERSION, | |
| LLM_KV_GENERAL_URL, | |
| LLM_KV_GENERAL_DESCRIPTION, | |
| LLM_KV_GENERAL_LICENSE, | |
| LLM_KV_GENERAL_SOURCE_URL, | |
| LLM_KV_GENERAL_SOURCE_HF_REPO, | |
| LLM_KV_VOCAB_SIZE, | |
| LLM_KV_CONTEXT_LENGTH, | |
| LLM_KV_EMBEDDING_LENGTH, | |
| LLM_KV_FEATURES_LENGTH, | |
| LLM_KV_BLOCK_COUNT, | |
| LLM_KV_LEADING_DENSE_BLOCK_COUNT, | |
| LLM_KV_FEED_FORWARD_LENGTH, | |
| LLM_KV_EXPERT_FEED_FORWARD_LENGTH, | |
| LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, | |
| LLM_KV_USE_PARALLEL_RESIDUAL, | |
| LLM_KV_TENSOR_DATA_LAYOUT, | |
| LLM_KV_EXPERT_COUNT, | |
| LLM_KV_EXPERT_USED_COUNT, | |
| LLM_KV_EXPERT_SHARED_COUNT, | |
| LLM_KV_EXPERT_WEIGHTS_SCALE, | |
| LLM_KV_EXPERT_WEIGHTS_NORM, | |
| LLM_KV_EXPERT_GATING_FUNC, | |
| LLM_KV_MOE_EVERY_N_LAYERS, | |
| LLM_KV_POOLING_TYPE, | |
| LLM_KV_LOGIT_SCALE, | |
| LLM_KV_DECODER_START_TOKEN_ID, | |
| LLM_KV_ATTN_LOGIT_SOFTCAPPING, | |
| LLM_KV_FINAL_LOGIT_SOFTCAPPING, | |
| LLM_KV_SWIN_NORM, | |
| LLM_KV_RESCALE_EVERY_N_LAYERS, | |
| LLM_KV_TIME_MIX_EXTRA_DIM, | |
| LLM_KV_TIME_DECAY_EXTRA_DIM, | |
| LLM_KV_RESIDUAL_SCALE, | |
| LLM_KV_EMBEDDING_SCALE, | |
| LLM_KV_TOKEN_SHIFT_COUNT, | |
| LLM_KV_INTERLEAVE_MOE_LAYER_STEP, | |
| LLM_KV_ATTENTION_HEAD_COUNT, | |
| LLM_KV_ATTENTION_HEAD_COUNT_KV, | |
| LLM_KV_ATTENTION_MAX_ALIBI_BIAS, | |
| LLM_KV_ATTENTION_CLAMP_KQV, | |
| LLM_KV_ATTENTION_KEY_LENGTH, | |
| LLM_KV_ATTENTION_VALUE_LENGTH, | |
| LLM_KV_ATTENTION_LAYERNORM_EPS, | |
| LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, | |
| LLM_KV_ATTENTION_GROUPNORM_EPS, | |
| LLM_KV_ATTENTION_GROUPNORM_GROUPS, | |
| LLM_KV_ATTENTION_CAUSAL, | |
| LLM_KV_ATTENTION_Q_LORA_RANK, | |
| LLM_KV_ATTENTION_KV_LORA_RANK, | |
| LLM_KV_ATTENTION_DECAY_LORA_RANK, | |
| LLM_KV_ATTENTION_ICLR_LORA_RANK, | |
| LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, | |
| LLM_KV_ATTENTION_GATE_LORA_RANK, | |
| LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, | |
| LLM_KV_ATTENTION_SLIDING_WINDOW, | |
| LLM_KV_ATTENTION_SCALE, | |
| LLM_KV_ATTENTION_KEY_LENGTH_MLA, | |
| LLM_KV_ATTENTION_VALUE_LENGTH_MLA, | |
| LLM_KV_ROPE_DIMENSION_COUNT, | |
| LLM_KV_ROPE_DIMENSION_SECTIONS, | |
| LLM_KV_ROPE_FREQ_BASE, | |
| LLM_KV_ROPE_SCALE_LINEAR, | |
| LLM_KV_ROPE_SCALING_TYPE, | |
| LLM_KV_ROPE_SCALING_FACTOR, | |
| LLM_KV_ROPE_SCALING_ATTN_FACTOR, | |
| LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, | |
| LLM_KV_ROPE_SCALING_FINETUNED, | |
| LLM_KV_ROPE_SCALING_YARN_LOG_MUL, | |
| LLM_KV_SPLIT_NO, | |
| LLM_KV_SPLIT_COUNT, | |
| LLM_KV_SPLIT_TENSORS_COUNT, | |
| LLM_KV_SSM_INNER_SIZE, | |
| LLM_KV_SSM_CONV_KERNEL, | |
| LLM_KV_SSM_STATE_SIZE, | |
| LLM_KV_SSM_TIME_STEP_RANK, | |
| LLM_KV_SSM_GROUP_COUNT, | |
| LLM_KV_SSM_DT_B_C_RMS, | |
| LLM_KV_WKV_HEAD_SIZE, | |
| LLM_KV_TOKENIZER_MODEL, | |
| LLM_KV_TOKENIZER_PRE, | |
| LLM_KV_TOKENIZER_LIST, | |
| LLM_KV_TOKENIZER_TOKEN_TYPE, | |
| LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, | |
| LLM_KV_TOKENIZER_SCORES, | |
| LLM_KV_TOKENIZER_MERGES, | |
| LLM_KV_TOKENIZER_BOS_ID, | |
| LLM_KV_TOKENIZER_EOS_ID, | |
| LLM_KV_TOKENIZER_EOT_ID, | |
| LLM_KV_TOKENIZER_EOM_ID, | |
| LLM_KV_TOKENIZER_UNK_ID, | |
| LLM_KV_TOKENIZER_SEP_ID, | |
| LLM_KV_TOKENIZER_PAD_ID, | |
| LLM_KV_TOKENIZER_CLS_ID, | |
| LLM_KV_TOKENIZER_MASK_ID, | |
| LLM_KV_TOKENIZER_ADD_BOS, | |
| LLM_KV_TOKENIZER_ADD_EOS, | |
| LLM_KV_TOKENIZER_ADD_SEP, | |
| LLM_KV_TOKENIZER_ADD_PREFIX, | |
| LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, | |
| LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, | |
| LLM_KV_TOKENIZER_HF_JSON, | |
| LLM_KV_TOKENIZER_RWKV, | |
| LLM_KV_TOKENIZER_CHAT_TEMPLATE, | |
| LLM_KV_TOKENIZER_FIM_PRE_ID, | |
| LLM_KV_TOKENIZER_FIM_SUF_ID, | |
| LLM_KV_TOKENIZER_FIM_MID_ID, | |
| LLM_KV_TOKENIZER_FIM_PAD_ID, | |
| LLM_KV_TOKENIZER_FIM_REP_ID, | |
| LLM_KV_TOKENIZER_FIM_SEP_ID, | |
| LLM_KV_ADAPTER_TYPE, | |
| LLM_KV_ADAPTER_LORA_ALPHA, | |
| LLM_KV_POSNET_EMBEDDING_LENGTH, | |
| LLM_KV_POSNET_BLOCK_COUNT, | |
| LLM_KV_CONVNEXT_EMBEDDING_LENGTH, | |
| LLM_KV_CONVNEXT_BLOCK_COUNT, | |
| LLM_KV_CLASSIFIER_OUTPUT_LABELS, | |
| LLM_KV_SHORTCONV_L_CACHE, | |
| // deprecated: | |
| LLM_KV_TOKENIZER_PREFIX_ID, | |
| LLM_KV_TOKENIZER_SUFFIX_ID, | |
| LLM_KV_TOKENIZER_MIDDLE_ID, | |
| }; | |
| enum llm_tensor { | |
| LLM_TENSOR_TOKEN_EMBD, | |
| LLM_TENSOR_TOKEN_EMBD_NORM, | |
| LLM_TENSOR_TOKEN_TYPES, | |
| LLM_TENSOR_POS_EMBD, | |
| LLM_TENSOR_OUTPUT, | |
| LLM_TENSOR_OUTPUT_NORM, | |
| LLM_TENSOR_ROPE_FREQS, | |
| LLM_TENSOR_ROPE_FACTORS_LONG, | |
| LLM_TENSOR_ROPE_FACTORS_SHORT, | |
| LLM_TENSOR_ATTN_Q, | |
| LLM_TENSOR_ATTN_K, | |
| LLM_TENSOR_ATTN_V, | |
| LLM_TENSOR_ATTN_QKV, | |
| LLM_TENSOR_ATTN_OUT, | |
| LLM_TENSOR_ATTN_NORM, | |
| LLM_TENSOR_ATTN_NORM_2, | |
| LLM_TENSOR_ATTN_OUT_NORM, | |
| LLM_TENSOR_ATTN_POST_NORM, | |
| LLM_TENSOR_ATTN_ROT_EMBD, | |
| LLM_TENSOR_FFN_GATE_INP, | |
| LLM_TENSOR_FFN_GATE_INP_SHEXP, | |
| LLM_TENSOR_FFN_NORM, | |
| LLM_TENSOR_FFN_POST_NORM, | |
| LLM_TENSOR_FFN_GATE, | |
| LLM_TENSOR_FFN_DOWN, | |
| LLM_TENSOR_FFN_UP, | |
| LLM_TENSOR_FFN_ACT, | |
| LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility | |
| LLM_TENSOR_FFN_GATE_EXP, | |
| LLM_TENSOR_FFN_UP_EXP, | |
| LLM_TENSOR_FFN_NORM_EXPS, | |
| LLM_TENSOR_FFN_DOWN_EXPS, // merged experts | |
| LLM_TENSOR_FFN_GATE_EXPS, | |
| LLM_TENSOR_FFN_UP_EXPS, | |
| LLM_TENSOR_FFN_DOWN_SHEXP, | |
| LLM_TENSOR_FFN_GATE_SHEXP, | |
| LLM_TENSOR_FFN_UP_SHEXP, | |
| LLM_TENSOR_FFN_EXP_PROBS_B, | |
| LLM_TENSOR_ATTN_Q_NORM, | |
| LLM_TENSOR_ATTN_K_NORM, | |
| LLM_TENSOR_LAYER_OUT_NORM, | |
| LLM_TENSOR_POST_ATTN_NORM, | |
| LLM_TENSOR_POST_MLP_NORM, | |
| LLM_TENSOR_PER_LAYER_TOKEN_EMBD, // gemma3n | |
| LLM_TENSOR_PER_LAYER_MODEL_PROJ, // gemma3n | |
| LLM_TENSOR_PER_LAYER_INP_GATE, // gemma3n | |
| LLM_TENSOR_PER_LAYER_PROJ, // gemma3n | |
| LLM_TENSOR_PER_LAYER_PROJ_NORM, // gemma3n | |
| LLM_TENSOR_PER_LAYER_POST_NORM, // gemma3n | |
| LLM_TENSOR_ALTUP_PROJ, // gemma3n | |
| LLM_TENSOR_ALTUP_UNEMBD_PROJ, // gemma3n | |
| LLM_TENSOR_ALTUP_CORRECT_COEF, // gemma3n | |
| LLM_TENSOR_ALTUP_CORRECT_SCALE, // gemma3n | |
| LLM_TENSOR_ALTUP_PREDICT_COEF, // gemma3n | |
| LLM_TENSOR_ALTUP_ROUTER, // gemma3n | |
| LLM_TENSOR_ALTUP_ROUTER_NORM, // gemma3n | |
| LLM_TENSOR_LAUREL_L, // gemma3n | |
| LLM_TENSOR_LAUREL_R, // gemma3n | |
| LLM_TENSOR_LAUREL_POST_NORM, // gemma3n | |
| LLM_TENSOR_SSM_IN, | |
| LLM_TENSOR_SSM_CONV1D, | |
| LLM_TENSOR_SSM_X, | |
| LLM_TENSOR_SSM_DT, | |
| LLM_TENSOR_SSM_DT_NORM, | |
| LLM_TENSOR_SSM_A, | |
| LLM_TENSOR_SSM_B_NORM, | |
| LLM_TENSOR_SSM_C_NORM, | |
| LLM_TENSOR_SSM_D, | |
| LLM_TENSOR_SSM_NORM, | |
| LLM_TENSOR_SSM_OUT, | |
| LLM_TENSOR_TIME_MIX_W0, | |
| LLM_TENSOR_TIME_MIX_W1, | |
| LLM_TENSOR_TIME_MIX_W2, | |
| LLM_TENSOR_TIME_MIX_A0, | |
| LLM_TENSOR_TIME_MIX_A1, | |
| LLM_TENSOR_TIME_MIX_A2, | |
| LLM_TENSOR_TIME_MIX_V0, | |
| LLM_TENSOR_TIME_MIX_V1, | |
| LLM_TENSOR_TIME_MIX_V2, | |
| LLM_TENSOR_TIME_MIX_G1, | |
| LLM_TENSOR_TIME_MIX_G2, | |
| LLM_TENSOR_TIME_MIX_K_K, | |
| LLM_TENSOR_TIME_MIX_K_A, | |
| LLM_TENSOR_TIME_MIX_R_K, | |
| LLM_TENSOR_TIME_MIX_LERP_X, | |
| LLM_TENSOR_TIME_MIX_LERP_W, | |
| LLM_TENSOR_TIME_MIX_LERP_K, | |
| LLM_TENSOR_TIME_MIX_LERP_V, | |
| LLM_TENSOR_TIME_MIX_LERP_R, | |
| LLM_TENSOR_TIME_MIX_LERP_G, | |
| LLM_TENSOR_TIME_MIX_LERP_FUSED, | |
| LLM_TENSOR_TIME_MIX_FIRST, | |
| LLM_TENSOR_TIME_MIX_DECAY, | |
| LLM_TENSOR_TIME_MIX_DECAY_W1, | |
| LLM_TENSOR_TIME_MIX_DECAY_W2, | |
| LLM_TENSOR_TIME_MIX_KEY, | |
| LLM_TENSOR_TIME_MIX_VALUE, | |
| LLM_TENSOR_TIME_MIX_RECEPTANCE, | |
| LLM_TENSOR_TIME_MIX_GATE, | |
| LLM_TENSOR_TIME_MIX_LN, | |
| LLM_TENSOR_TIME_MIX_OUTPUT, | |
| LLM_TENSOR_CHANNEL_MIX_LERP_K, | |
| LLM_TENSOR_CHANNEL_MIX_LERP_R, | |
| LLM_TENSOR_CHANNEL_MIX_KEY, | |
| LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, | |
| LLM_TENSOR_CHANNEL_MIX_VALUE, | |
| LLM_TENSOR_ATTN_Q_A, | |
| LLM_TENSOR_ATTN_Q_B, | |
| LLM_TENSOR_ATTN_KV_A_MQA, | |
| LLM_TENSOR_ATTN_KV_B, | |
| LLM_TENSOR_ATTN_K_B, | |
| LLM_TENSOR_ATTN_V_B, | |
| LLM_TENSOR_ATTN_Q_A_NORM, | |
| LLM_TENSOR_ATTN_KV_A_NORM, | |
| LLM_TENSOR_ATTN_SUB_NORM, | |
| LLM_TENSOR_FFN_SUB_NORM, | |
| LLM_TENSOR_DEC_ATTN_NORM, | |
| LLM_TENSOR_DEC_ATTN_Q, | |
| LLM_TENSOR_DEC_ATTN_K, | |
| LLM_TENSOR_DEC_ATTN_V, | |
| LLM_TENSOR_DEC_ATTN_OUT, | |
| LLM_TENSOR_DEC_ATTN_REL_B, | |
| LLM_TENSOR_DEC_CROSS_ATTN_NORM, | |
| LLM_TENSOR_DEC_CROSS_ATTN_Q, | |
| LLM_TENSOR_DEC_CROSS_ATTN_K, | |
| LLM_TENSOR_DEC_CROSS_ATTN_V, | |
| LLM_TENSOR_DEC_CROSS_ATTN_OUT, | |
| LLM_TENSOR_DEC_CROSS_ATTN_REL_B, | |
| LLM_TENSOR_DEC_FFN_NORM, | |
| LLM_TENSOR_DEC_FFN_GATE, | |
| LLM_TENSOR_DEC_FFN_DOWN, | |
| LLM_TENSOR_DEC_FFN_UP, | |
| LLM_TENSOR_DEC_OUTPUT_NORM, | |
| LLM_TENSOR_ENC_ATTN_NORM, | |
| LLM_TENSOR_ENC_ATTN_Q, | |
| LLM_TENSOR_ENC_ATTN_K, | |
| LLM_TENSOR_ENC_ATTN_V, | |
| LLM_TENSOR_ENC_ATTN_OUT, | |
| LLM_TENSOR_ENC_ATTN_REL_B, | |
| LLM_TENSOR_ENC_FFN_NORM, | |
| LLM_TENSOR_ENC_FFN_GATE, | |
| LLM_TENSOR_ENC_FFN_DOWN, | |
| LLM_TENSOR_ENC_FFN_UP, | |
| LLM_TENSOR_ENC_OUTPUT_NORM, | |
| LLM_TENSOR_CLS, | |
| LLM_TENSOR_CLS_OUT, | |
| LLM_TENSOR_CONV1D, | |
| LLM_TENSOR_CONVNEXT_DW, | |
| LLM_TENSOR_CONVNEXT_NORM, | |
| LLM_TENSOR_CONVNEXT_PW1, | |
| LLM_TENSOR_CONVNEXT_PW2, | |
| LLM_TENSOR_CONVNEXT_GAMMA, | |
| LLM_TENSOR_POS_NET_CONV1, | |
| LLM_TENSOR_POS_NET_CONV2, | |
| LLM_TENSOR_POS_NET_NORM, | |
| LLM_TENSOR_POS_NET_NORM1, | |
| LLM_TENSOR_POS_NET_NORM2, | |
| LLM_TENSOR_POS_NET_ATTN_NORM, | |
| LLM_TENSOR_POS_NET_ATTN_Q, | |
| LLM_TENSOR_POS_NET_ATTN_K, | |
| LLM_TENSOR_POS_NET_ATTN_V, | |
| LLM_TENSOR_POS_NET_ATTN_OUT, | |
| LLM_TENSOR_SHORTCONV_CONV, | |
| LLM_TENSOR_SHORTCONV_INPROJ, | |
| LLM_TENSOR_SHORTCONV_OUTPROJ, | |
| }; | |
| enum llm_tensor_layer { | |
| LLM_TENSOR_LAYER_INPUT, | |
| LLM_TENSOR_LAYER_REPEATING, | |
| LLM_TENSOR_LAYER_OUTPUT, | |
| }; | |
| struct LLM_KV { | |
| LLM_KV(llm_arch arch, const char * suffix = nullptr); | |
| llm_arch arch; | |
| const char * suffix; | |
| std::string operator()(llm_kv kv) const; | |
| }; | |
| // helper to handle gguf constants | |
| // usage: | |
| // | |
| // const auto tn = LLM_TN(LLM_ARCH_LLAMA); | |
| // | |
| // std::string name = tn(LLM_TENSOR_OUTPUT); -> "output" | |
| // std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias" | |
| // std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight" | |
| // | |
| struct LLM_TN_IMPL { | |
| const llm_arch arch; | |
| const llm_tensor tensor; | |
| const char * const suffix; | |
| const int bid; | |
| const int xid; | |
| std::string str() const; | |
| operator std::string() const { | |
| return str(); | |
| } | |
| friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) { | |
| return str == tn.str(); | |
| } | |
| friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) { | |
| return str != tn.str(); | |
| } | |
| }; | |
| struct LLM_TN { | |
| LLM_TN(llm_arch arch) : arch(arch) {} | |
| llm_arch arch; | |
| LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const { | |
| return { arch, tensor, suffix, bid, xid }; | |
| } | |
| LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const { | |
| return { arch, tensor, nullptr, bid, xid }; | |
| } | |
| }; | |
| struct llm_tensor_info { | |
| llm_tensor_layer layer; | |
| ggml_op op; | |
| }; | |
| const char * llm_arch_name(llm_arch arch); | |
| llm_arch llm_arch_from_string(const std::string & name); | |
| const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor); | |
| bool llm_arch_is_recurrent(const llm_arch & arch); | |
| bool llm_arch_is_hybrid (const llm_arch & arch); | |
| bool llm_arch_is_diffusion(const llm_arch & arch); | |