gistfile1.txt

be real: 10 GB is nothing in pretraining terms. it *can* move the needle for linux/bash—but only if you do it right (CPT + careful mixing), otherwise you just induce weird overfitting + forget general skills.

---

## what you actually want (afaict)

you’re describing **continued pretraining (CPT)** on top of something like Google DeepMind’s Gemma.

goal:

* inject *distributional bias* toward linux / bash / security
* not wreck general reasoning

---

## first: scale reality

10 GB text ≈

* ~2–3B tokens (depending on entropy + formatting)

compare:

* base models trained on **trillions** of tokens

→ so your data is like a *strong prior tilt*, not a rewrite

this is fine if:

* data is high signal (man pages, real scripts, incident reports)
* you don’t overtrain

---

## 3 viable approaches (ranked)

### 1) CPT (what you asked)

keep training LM objective on your corpus

**BUT**
you *must* mix in general data (5–20%) or you’ll degrade

---

### 2) CPT → SFT (better)

1. CPT on raw corpus
2. SFT on curated linux tasks (commands, debugging, etc.)

this actually gives you usable behavior

---

### 3) skip CPT, do SFT only (often enough)

if your goal is:

* “write bash”
* “debug linux issues”

you can get 80% there with SFT + good dataset

---

## hardware (real numbers)

assume:

* Gemma 7B or 13B
* sequence length 2k–4k
* bf16

---

### minimal viable (scrappy, works)

**Gemma 7B CPT**

* 1–2× A100 80GB *or*
* 2–4× H100
* or even 1× H100 w/ gradient checkpointing

**throughput**

* ~50–150k tokens/sec depending on setup

**time**

* 2–3B tokens → ~6–24 hours

---

### sane setup (what you’d actually run)

**Gemma 7B**

* 4× H100 or B200 (since you’re already playing there)
* FSDP or DeepSpeed ZeRO-3

→ finishes in a few hours, stable

---

### if you go 13B+

* 8× H100/B200 preferred
* or accept slow training + more instability

---

## config that matters (people screw this up)

**learning rate**

* tiny: `1e-5 – 5e-5`
* you are nudging, not relearning

**epochs**

* 1–2 max
* more = overfit + catastrophic forgetting

**mixing**

* 80–95% your corpus
* 5–20% general text

**tokenization**

* don’t mess this up
* bash/code spacing matters a lot

---

## data matters more than compute

your 10GB should be:

good:

* man pages
* real shell histories
* infra repos
* incident writeups
* security advisories (CVE writeups)

bad:

* random scraped tutorials
* SEO garbage
* duplicate stackoverflow spam

dedupe aggressively or you’re just memorizing noise

---

## what “better at linux” actually means

CPT improves:

* command recall
* syntax priors
* familiarity with flags / patterns

it does **NOT reliably improve**

* multi-step reasoning
* debugging chains

→ that’s SFT / agent loop territory

---

## the real play (imo)

if i were you given your infra + B200 experiments:

* do **light CPT (1 epoch)** on your 10GB
* then build **synthetic SFT dataset**:

  * prompt: “fix this broken bash script”
  * output: corrected + explanation
* then run LoRA SFT

→ this compounds way harder than CPT alone

---

## blunt take

most people try to “add knowledge” via CPT and end up:

* slightly worse general model
* marginal gains in domain

the edge is:

* tight eval loop (bash execution sandbox)
* generate → run → score → fine-tune again

---

if you want, i can sketch:

* exact DeepSpeed config
* token/sec estimates on your B200 nodes
* or how to auto-generate 100k high-quality bash training pairs (that’s where the alpha is)