Added cmb build process

Added the community build process doc

Signed-off-by: JJ Asghar <awesome@ibm.com>

Author: jjasghar

docs/cmb/build_process.md

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+
+!!! note
+    This document is the Community Build Process, these are the general steps to get the cmb built.
+
+
+## Add the PRs to the local tree
+
+Add the PRs you want to be built into the run. Tag the PRs with "cmb-running."
+
+```bash
+mkdir -p compositional_skills/general/synonyms
+vi compositional_skills/general/synonyms/attribution.txt
+vi compositional_skills/general/synonyms/qna.yaml
+```
+
+## Verify changes
+```bash
+ilab taxonomy diff
+```
+
+!!! warning
+    `~/.local/share/instructlab/datasets` -- should be empty before starting
+     Every gpu should be "empty", or `0%` check with `nvidia-smi`
+
+## Create the data
+```bash
+ilab data generate
+```
+
+## Run the training after the generate is complete
+```bash
+ilab model train --strategy lab-multiphase --phased-phase1-data ~/.local/share/instructlab/datasets/knowledge_train_msgs_XXXXXXX.jsonl --phased-phase2-data ~/.local/share/instructlab/datasets/skills_train_msgs_XXXXXXX.jsonl
+```
+
+## Post training evaluation steps
+
+If you want to send a sanity check, you can set these two variables to do a subset of the training:
+```bash
+export INSTRUCTLAB_EVAL_FIRST_N_QUESTIONS=10 # mtbench
+export INSTRUCTLAB_EVAL_MMLU_MIN_TASKS=true # mmlu
+```
+
+(optional in case of sanity of a specific Sample Model creation)
+```bash
+ilab model evaluate --benchmark mt_bench --model ~/.local/share/instructlab/checkpoints/hf_format/samples_XXXXXX
+```
+!!! tip
+    We should do the revaluation because we want to reverify the numbers before going any farther.
+
+## General Benchmarking
+
+- `mmlu`: general model knowledge, general facts, it's a knowledge number out of 100
+- `mt_bench`: is a skill based, extraction, etc, out of 10
+!!! note
+    we want around 7.1 for `mt_bench` average for a model candidate
+
+## Specific Benchmarking
+`mmlu_branch`: these are specific to the general knowledge
+
+```bash
+ilab model evaluate --benchmark mmlu_branch --model ~/.local/share/checkpoints/hf_format/<checkpoint> --tasks-dir ~/.local/share/instructlab/datasets/<node-dataset> --base-model ~/.cache/instructlab/models/granite-7b-redhat-lab
+```
+
+`mt_bench_branch`:  these are specific for the skills
+```bash
+ilab model evaluate --benchmark mt_bench_branch --model ~/.local/share/checkpoints/hf_format/<checkpoint> --taxonomy-path ~/.local/share/instructlab/taxonomy --judge-model ~/.cache/instructlab/models/prometheus-8x7b-v2-0 --base-model ~/.cache/instructlab/models/granite-7b-redhat-lab --base-branch main --branch main
+```
+
+## Hosting the release candidates
+
+rsync over the files
+```bash
+mkdir $(date +%F)
+cd $(date +%F)
+rsync --info=progress2 -avz -e <USERNAME>@<REMOTE>:~/.local/share/checkpoints/hf_format/samples_xxxxx ./
+```
+
+Set up (if needed)
+```bash
+python3.11 -m venv venv
+source venv/bin/activate
+pip install vllm
+./run.sh
+```
+
+`run.sh`
+```bash
+#!/bin/bash
+
+DIRECTORY=$1
+
+DATE=$(date +%F)
+RANDOM_STRING=$(tr -dc A-Za-z0-9 </dev/urandom | head -c 10; echo)
+RANDOM_PORT=$(shuf -i 8001-8800 -n 1)
+API_KEY=$RANDOM_STRING-$DATE
+
+echo "$DIRECTORY,$API_KEY,$RANDOM_PORT" >> model_hosting.csv
+
+echo "ilab model chat --endpoint-url http://cmb-staging.DOMAIN.xx:$RANDOM_PORT/v1 --api-key $API_KEY --model $DIRECTORY" >> model_ilab_scripting.sh
+
+python -m vllm.entrypoints.openai.api_server --model $DIRECTORY --api-key $API_KEY --host 0.0.0.0 --port $RANDOM_PORT --tensor-parallel-size 2
+```
+
+Find the `ilab` random command to host the model, send that on after the PR letter
+```
+cat model_ilab_scripting.sh
+```
+## Form letter for PRs
+
+Hi! 👋
+Thank you for submitting this PR. We are ready to do some validation now, and we have a few candidates to see if they improve the model.
+We some resources to run these release candidates, but we need _you_ to help us. Can you reach out to me either on Slack (@awesome) or email me at awesomeATinstructlab.ai so I can get you access via `ilab model chat`?
+We can only run these models for a "week" or so, so please reach out as soon as possible and tell me which one is best for you on this PR.
+
+## With confirmed success
+
+With confirmed success, tag the PR with "ready-for-merge" and remove the "community-build-ready" tags. Wait till the "week" before shutting down the staging instance, and merge in all the ones that have been tagged.
+
+## Steps to Merge and Release
+
+After you have merged in the PRs to the taxonomy, now you need to push this to huggingface, if you don't have access to HuggingFace, you will need to find someone to add you to it ;).
+
+1) Clone down the repository on the staging box if you haven't already
+```bash
+git clone https://huggingface.co/instructlab/granite-7b-lab
+cd granite-7b-lab
+vi .git/config
+# url = git@hf.co:instructlab/granite-7b-lab
+# verify you can authenticate with hf.com: ssh -T git@hf.co
+```
+2) Copy in the `samples_xxxx` into the granite-7b-lab
+3) `git add . && git commit`
+4) Write up a good commit message
+5) tag and push
+```bash
+git tag cmb-run-XXXXX
+git push origin main
+git push origin cmb-run-XXXXX
+```
+
+## Convert to `gguf`
+```bash
+git clone https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp/
+pip install -r requirements.txt
+make -j8
+./convert_hf_to_gguf.py ../granite-7b-lab --outfile granite-7b-fp16.gguf
+./llama-quantize granite-7b-fp16.gguf granite-7b-_Q4_K_M.gguf Q4_K_M/
+./llama-cli -m granite-7b-_Q4_K_M.gguf -p "who is batman?" -n 128
+```
+