$ python train_model.py --epochs 100 --lr 0.001
> Loading dataset... [████████████] 100%
> Epoch 1/100 — loss: 0.4821 — acc: 0.7234
> Epoch 2/100 — loss: 0.3912 — acc: 0.8102
import torch; import transformers
from langchain import LLMChain, PromptTemplate
model = AutoModelForCausalLM.from_pretrained("gpt2")
> Fine-tuning... [████████░░░░] 67%
agent.run("Analyse ce document et génère un rapport")
> Tokens generated: 1,247 — Latency: 0.34s
def create_agent(tools, llm, memory):
    return AgentExecutor.from_agent_and_tools(...)
> Agent initialized — Ready for deployment
curl -X POST /api/agents/run -d '{"prompt":"..."}'
> Response: {"status":"ok","output":"..."}
$ pip install openai anthropic langchain
> Successfully installed 47 packages
vectorstore = FAISS.from_documents(docs, embeddings)
> Index built — 12,847 vectors stored
$ python evaluate.py --model gpt-4 --benchmark mmlu
> Score: 86.4% — Rank: Top 3%
chain = LLMChain(llm=llm, prompt=prompt_template)
> Chain ready — Streaming enabled
$ python train_model.py --epochs 100 --lr 0.001
> Loading dataset... [████████████] 100%
import torch; import transformers
from langchain import LLMChain, PromptTemplate
model = AutoModelForCausalLM.from_pretrained("gpt2")
agent.run("Analyse ce document et génère un rapport")
def create_agent(tools, llm, memory):
vectorstore = FAISS.from_documents(docs, embeddings)
$ pip install openai anthropic langchain
> Successfully installed 47 packages