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
File size: 4,118 Bytes
305a42c | 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 | #pragma once
#include "llama.h"
#include <memory>
struct llama_ubatch;
class llama_batch_allocr;
class llama_io_write_i;
class llama_io_read_i;
struct llama_memory_params {
// kv cache
ggml_type type_k;
ggml_type type_v;
// use full-size SWA cache
bool swa_full;
};
enum llama_memory_status {
LLAMA_MEMORY_STATUS_SUCCESS = 0,
LLAMA_MEMORY_STATUS_NO_UPDATE,
LLAMA_MEMORY_STATUS_FAILED_PREPARE,
LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
};
// helper function for combining the status of two memory contexts
// useful for implementing hybrid memory types (e.g. iSWA)
llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1);
// helper function for checking if a memory status indicates a failure
bool llama_memory_status_is_fail(llama_memory_status status);
// the interface for managing the memory context during batch processing
// this interface is implemented per memory type. see:
// - llama_kv_cache_unified_context
// - llama_kv_cache_unified_iswa_context
// ...
//
// the only method that should mutate the memory and the memory context is llama_memory_i::apply()
struct llama_memory_context_i {
virtual ~llama_memory_context_i() = default;
// consume the current ubatch from the context and proceed to the next one
// return false if we are done
virtual bool next() = 0;
// apply the memory state for the current ubatch to the memory object
// return false on failure
virtual bool apply() = 0;
// get the current ubatch
virtual const llama_ubatch & get_ubatch() const = 0;
// get the status of the memory context - used for error handling and checking if any updates would be applied
virtual llama_memory_status get_status() const = 0;
};
using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
// general concept of LLM memory
// the KV cache is a type of LLM memory, but there can be other types
struct llama_memory_i {
virtual ~llama_memory_i() = default;
// split the input batch into a set of ubatches and verify that they can fit into the cache
// return a context object containing the ubatches and memory state required to process them
// check the llama_memory_context_i::get_status() for the result
virtual llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) = 0;
// simulate full cache, used for allocating worst-case compute buffers
virtual llama_memory_context_ptr init_full() = 0;
// prepare for any pending memory updates, such as shifts, defrags, etc.
// status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
// getters
virtual bool get_can_shift() const = 0;
//
// ops
//
// if data == true, the data buffers will also be cleared together with the metadata
virtual void clear(bool data) = 0;
virtual bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) = 0;
virtual void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0;
virtual void seq_keep(llama_seq_id seq_id) = 0;
virtual void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) = 0;
virtual void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) = 0;
virtual llama_pos seq_pos_min(llama_seq_id seq_id) const = 0;
virtual llama_pos seq_pos_max(llama_seq_id seq_id) const = 0;
//
// state write/read
//
virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
};
using llama_memory_ptr = std::unique_ptr<llama_memory_i>;
// TODO: temporary until the llama_kv_cache is removed from the public API
struct llama_kv_cache : public llama_memory_i {
virtual ~llama_kv_cache() = default;
};
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