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Here's a concise cheatsheet for Mustache templating:

Basic Syntax

• Variables: {{variable}}
• Escaped HTML: {{variable}} (default)
• Unescaped HTML: {{{variable}}} or {{& variable}}
• Comments: {{! comment }}

Sections

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<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times

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Typical good structure:

• Title
  • Overview.
• Table of Contents
• Roadmap
• Setup
• Configuration
• Data Loading
• Evaluation Function

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Here's a concise cheatsheet for JSON Schema:

Basic Structure

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "$id": "https://example.com/schema.json",
  "title": "Schema Title",
  "description": "Schema description",

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Basic Structure

Hydra uses YAML-like syntax for configuration files, typically with a .yaml extension.

key: value
nested:
  key: value
list:
  - item1

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Key Features

• PyTorch implementations of popular LLMs (Llama, Gemma, Mistral, Phi, Qwen)
• Training recipes for full fine-tuning, LoRA, QLoRA, DPO, PPO, QAT, knowledge distillation
• Memory efficiency and performance improvements
• YAML configs for easy recipe configuration
• Support for various dataset formats and prompt templates

Fine-tuning Recipes

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Prompt Engineering

Prompt Forge

• Allow you to quickly test system/user prompt along with self-defined examples
• prompt template with placeholder variables.
• Evaluators

Inference

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# Create an interactive bar chart with two bars per date
fig = go.Figure()
fig.add_trace(go.Bar(
    x=daily_formula_intake.index,
    y=daily_formula_intake.values,
    name='Formula'
))
fig.add_trace(go.Bar(
    x=daily_expressed_intake.index,
    y=daily_expressed_intake.values,

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class LanguageModel(nn.Module):
  def __init__(self):
      super().__init__()
      # Define transformer layers, embeddings, etc.

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# Pseudocode for estimating advantage function in RLHF using PPO

import numpy as np
import torch
import torch.nn.functional as F

def compute_ppo_loss(policy, old_policy, token_sequences, advantages, returns, clip_epsilon=0.2):
  """
  Compute the PPO loss for language model policy update