laurencecwj

syntax = "proto3";

package protos;

service HelloWorld {
  rpc SayHello (HelloRequest) returns (HelloResponse) {}
}

message HelloRequest {
  string name = 1;

laurencecwj

laurencecwj

input_embeds = self.transformer.wte(input_tokens) # look up tokens in embedding matrix

# Non-recurrent prelude
for block in self.transformer.prelude:
    input_embeds, attn_map = block(input_embeds)

# Main recurrence
for step in range(num_steps_recurrence):
  x = self.transformer.adapter(torch.cat([x, input_embeds], dim=-1)) # Adapter
  for block in self.transformer.core_block: # 4 Inner layers

laurencecwj

# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

laurencecwj

"""
This model integrates the MoE concept within a Transformer architecture. Each token's
representation is processed by a subset of experts, determined by the gating mechanism.
This architecture allows for efficient and specialized handling of different aspects of the
data, aiming for the adaptability and efficiency noted in the Mixtral 8x7B model's design
philosophy. The model activates only a fraction of the available experts for each token,
significantly reducing the computational resources needed compared to activating all experts
for all tokens.
"""

laurencecwj

DeepSeek R1 Distill: Complete Tutorial for Deployment & Fine-Tuning

This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:

1. Environment Setup
2. FastAPI Inference Server
3. Docker Configuration
4. Google Cloud Run Deployment
5. Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)

laurencecwj

package main

import (
	"log"
	"net/http"
	"net/url"

	"github.com/gin-gonic/gin"
	"github.com/google/uuid"

laurencecwj

cdef int _fib(int n):
    cdef int i
    cdef int a=0, b=1
    for i in range(n):
        a, b = a + b,a
    return a

def fib(n):
    return _fib(n)

laurencecwj

# Copyright 2018 Simon Davy
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in

laurencecwj

package main

import (
	"crypto/aes"
	"crypto/cipher"
	"fmt"
	"reflect"
	"strconv"
	"time"
)