pip install afrilink-sdkfrom afrilink import AfriLinkClientclient = AfriLinkClient()client.authenticate()  # DataSpires + HPC credentialsjob = client.finetune(    model="qwen2.5-0.5b",    training_mode="low",    data=your_dataframe,    gpus=1,    time_limit="01:00:00",    backend="cineca",)result = job.run(wait=True)if result["status"] == "completed":    client.download_model(result["job_id"], "./my-model")    from transformers import AutoModelForCausalLM, AutoTokenizer    from peft import PeftModel    base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")    model = PeftModel.from_pretrained(base, "./my-model")    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")    out = model.generate(**tokenizer("Hello!", return_tensors="pt"), max_new_tokens=64)    print(tokenizer.decode(out[0], skip_special_tokens=True))

pip install afrilink-sdk

job = client.train(    script="train_yolo.py",        # your training script    container="afrilink-yolo",      # pre-built container    data="./dataset/",              # uploaded automatically    data_config="dataset.yaml",     # config file    gpus=1,    time_limit="02:00:00",)result = job.run(wait=True)print(job.get_logs(tail=50))

# Re-authenticate and download completed modelsrecovery = client.recover_session("./recovered-models")print(recovery.re_authenticated)   # True if fresh cert obtainedprint(recovery.jobs)                # status of each tracked jobprint(recovery.files_retrieved)     # downloaded model directories# Or just re-authenticate without downloadingclient.recover_session()

import afrilinkafrilink/help          # index of all topicsafrilink/quickstart    # getting startedafrilink/auth          # authentication & sessionsafrilink/finetune      # finetune parameters & modesafrilink/specs         # models and GPU requirementsafrilink/datasets      # dataset formats and uploadafrilink/transfer      # file upload & downloadafrilink/jobs          # SLURM job management

from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel# Merge adapter into base modelbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")merged = PeftModel.from_pretrained(base, "./my-model").merge_and_unload()merged.save_pretrained("./my-model-merged")AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B").save_pretrained("./my-model-merged")# python convert_hf_to_gguf.py ./my-model-merged --outfile my-model.gguf# ./llama-quantize my-model.gguf my-model-q4.gguf Q4_K_M# ollama create my-model -f Modelfile  &&  ollama run my-model

from huggingface_hub import HfApiapi = HfApi(token="hf_...")repo_id = "your-username/my-finetuned-model"api.create_repo(repo_id, exist_ok=True)api.upload_folder(folder_path="./my-model", repo_id=repo_id)          # adapter onlyapi.upload_folder(folder_path="./my-model-merged", repo_id=repo_id)   # full merged modelapi.upload_file(path_or_fileobj="./my-model-q4.gguf",                path_in_repo="my-model-q4.gguf", repo_id=repo_id)       # GGUF

from huggingface_hub import InferenceClientclient = InferenceClient()# Generate text with an open LLMresponse = client.text_generation(    prompt="A HTTP POST request is used to ",    model="codellama/CodeLlama-7b-hf",    temperature=0.8,    max_new_tokens=50,    seed=42,    return_full_text=True,)print(response)# Chat completion with instruct modelsmessages = [    {"role": "system", "content": "You are an expert prompt engineer with artistic flair."},    {"role": "user", "content": "Write a concise prompt for a fun image... Only return the prompt."},]for token in client.chat_completion(    messages, model="meta-llama/Meta-Llama-3-8B-Instruct", max_tokens=250, stream=True, seed=42):    print(token.choices[0].delta.content, end="")