distill-1.7B is a domain-specific Expert Language Model — not a general-purpose chatbot. It does exactly one thing: compress and classify raw terminal output into structured, actionable summaries.

Built for the distill engine — an open-source CLI output compression tool.

What is distill?

distill is a tool that takes arbitrary command-line output and reduces it to only what matters. Instead of scrolling through 500 lines of npm install logs, you get:

PASS
24 packages installed, 0 vulnerabilities

Instead of parsing a wall of Terraform plan output, you get:

{"create": 3, "change": 12, "destroy": 0}

distill-1.7B is the brain behind distill — it's the model that understands CLI output and knows what's signal vs noise.

Why "Expert Language Model"?

Unlike general-purpose LLMs (ChatGPT, Claude, etc.) that can talk about anything, distill-1.7B is:

Trait	General LLM	distill-1.7B
Scope	Any topic	CLI output only
Size	70-400B params	1.7B params
Training data	Web crawl (trillions of tokens)	100k synthetic CLI outputs
Strengths	Conversation, reasoning, code	CLI compression, classification
Weaknesses	—	Can't chat, can't code, can't reason

It's an expert in the same way a radiologist is an expert — highly skilled in one narrow domain, not trying to be a general practitioner.

8 Specialized Tasks

Task	What it does	Example output
`pass_fail`	Did the command succeed or fail?	`PASS` / `FAIL Error: ...`
`safe_review`	Is this Terraform plan safe?	`SAFE` / `UNSAFE` / `REVIEW`
`terraform_plan`	Count resources created/changed/destroyed	`{"create":3,"change":12,"destroy":0}`
`json_extraction`	Pull JSON from noisy logs	`[{"name":"app","version":"2.1.0"}]`
`security_audit`	Count vulns by severity	`[{"severity":"high","count":2}]`
`test_result`	Test suite pass/fail?	`PASS\n4 passed, 0 failed`
`typescript_check`	Extract TS compiler errors	`error TS2741: Property 'x' is missing`
`generic`	Free-form summary of any CLI output	`24 packages installed`

Performance

Metric	Value
Overall accuracy	95%
Tasks at 100%	6 of 8
Base model	Qwen3-1.7B
Training	LoRA rank 32, 4000 iterations
Dataset	100k synthetic CLI outputs
Training hardware	Apple M4 Max, 128 GB RAM

Available Formats

Repo	Format	Size	Platform
distill-1.7B-MLX	MLX fp16	3.2 GB	macOS (Apple Silicon)
distill-1.7B-4bit-MLX	MLX 4-bit	1.0 GB	macOS (Apple Silicon)
distill-1.7B-GGUF	GGUF fp16	4.1 GB	Cross-platform
distill-1.7B-4bit-GGUF	GGUF Q4_K_M	1.2 GB	Cross-platform

All formats achieve identical 95% accuracy — pick based on your platform and size preference.

Usage

python

from mlx_lm import load, generate

model, tokenizer = load("samuelfaj/distill-1.7B-MLX")

messages = [
    {"role": "system", "content": "You are distill. Compress CLI output concisely."},
    {"role": "user", "content": "Command output:\nnpm test\n4 tests passed, 0 failed"}
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
result = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(result)

Project

This model powers distill — a CLI output compression engine. The training code and dataset generation pipeline are available in the repository.

Full Distill Collection