migtissera

base_model: /path/to/Mistral-7B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:

olafgeibig

FROM ./nous-hermes-2-solar-10.7b.Q5_K_M.gguf
PARAMETER num_ctx 4096
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

mberman84

1. # create new .py file with code found below
2. # install ollama
3. # install model you want “ollama run mistral”
4. conda create -n autogen python=3.11
5. conda activate autogen
6. which python
7. python -m pip install pyautogen
7. ollama run mistral
8. ollama run codellama
9. # open new terminal

mberman84

import os
import autogen
import memgpt.autogen.memgpt_agent as memgpt_autogen
import memgpt.autogen.interface as autogen_interface 
import memgpt.agent as agent
import memgpt.system as system
import memgpt.utils as utils
import memgpt.presets as presets
import memgpt.constants as constants
import memgpt.personas.personas as personas

miagkyi

Fine-tuning llama 2 7B to analyze financial reports and write “funny” tweets

Sharing some insights from a recent weekend fun project where I tried to analyze and summarize financial reports using a fine-tuned LLM.

My initial goal was to train a model to summarize the annual/quarterly financial reports of public companies (aka 10-K / 10-Q). But, realizing that straightforward financial summaries are boring, I thought of tuning LLM to generate sarcastic summaries of these reports. Something short I could post on Twitter.

Data exploration and dataset prep

Working with financial reports ain’t easy. You download them in html format, they’re pretty dense with ~100 pages filled with tables that can be tough to parse, many legal disclaimers and various useless info. I knew I wanted to get 3-5 funny tweets as an output from a report. But I spent quite some time figuring out what data to actually input to get the result - a page, a section, a table?

bonadio

import autogen
from user_proxy_webagent import UserProxyWebAgent
import asyncio

config_list = [
    {
        "model": "gpt-3.5-turbo",
        # "api_key": "<YOUR KEY HERE>"
    }
]

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

yzdbg

Automating Daily Reports, because fuck it, really...

Each day at our company, developers are required to document their activities, painstakingly jotting down their daily work and future plans. A monotonous chore that I just really dislike.

So now, there's a scribe for that :

Code