samuelfaj/distill-1.7B-MLX

distill-1.7B — Expert Language Model for CLI Output

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

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.

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