Natural-language → shell command generator, drop-in alternative to whatisit-nl2sh's nl2sh-1.5b (same base model family: Qwen2.5-Coder-1.5B-Instruct), trained on a broader, hardened data mix.

Measured on InterCode-ALFA (300 tasks, temp 0, max_tokens 64, unmodified upstream scorer with embedding heuristic at threshold 0.75):

model	size	pass rate
GPT-4o (published)	cloud	0.730
this model, Q4_K_M (941 MB)	941M	0.6567
nl2sh-3b (upstream, published)	1.9G	0.657
this model, Q4_K_M + imatrix	941M	0.6500
this model, Q6_K	1.2G	0.6433
this model, Q8_0	1.6G	0.6367
nl2sh-1.5b (upstream, published)	941M	0.620
nl2sh-1.5b (re-measured on same rig)	941M	0.590

Same-size Q4_K_M beats the upstream default by +3.7 points over its published score and +6.7 points measured head-to-head on identical hardware/protocol. (Quant-to-quant differences of 1–2 pts are within the ±5.5 pt binomial noise at n=300 — treat all variants as one ~0.65 blob; Q4_K_M is recommended as it holds the top as-measured score at the smallest size.)

Files

file	size	use
`qcoder-nl2sh-q4_k_m.gguf`	941M	default pick — best measured score
`qcoder-nl2sh-q4_k_m_imx.gguf`	941M	imatrix variant (`imatrix.gguf`, corpus = NL2SH pairs)
`qcoder-nl2sh-q6_k.gguf`	1.2G	quality step-up
`qcoder-nl2sh-q8_0.gguf`	1.6G	near-lossless (+0.14% ppl)
`qcoder-nl2sh-f16.gguf`	2.9G	reference
`imatrix.gguf`	1.9M	importance matrix used for `_imx` build
`lora-adapter/`	~180M	PEFT LoRA adapter

Perplexity ladder (held-out NL2SH pairs): f16 4.649 · Q8 4.655 · Q6 4.665 · Q4+imx 4.720 · Q4 4.789.

Robustness

Trained with organic chatter pairs so arbitrary input always yields a sane, harmless command instead of garbage:

> hello          → echo hello
> thanks         → echo "you're welcome!"
> who am i       → whoami
> qwerty         → pwd
> what time is it→ date

Training setup


Base	Qwen2.5-Coder-1.5B-Instruct
Data	120,349 NL/command pairs: NL2SH-ALFA train + tldr-pages + commandlinefu + NL2Bash + cli-1m sample + git-instruction + organic robustness set; length-capped ≤56 tokens, single-line, deduped, decontaminated vs InterCode-ALFA test
LoRA rank / alpha / dropout	32 / 64 / 0.05, all attention+MLP projections
LR / schedule	2e-4, cosine, 3% warmup
Epochs / batch / seq	1 / 32 effective (8×4) / 224 grouped-by-length
Loss	assistant-only (chat-template masked)
Precision / seed	bf16, seed 42
Hardware	AMD Radeon RX 9070 XT (gfx1201), 2h12m

Final train loss 0.904 avg, token accuracy 82.7%.

Note for RDNA4/ROCm trainers: requires PYTORCH_HIP_ALLOC_CONF=roundup_power2_divisions:16 and hipBLASLt disabled (TORCH_BLAS_PREFER_HIPBLASLT=0 ROCBLAS_USE_HIPBLASLT=0 DISABLE_ADDMM_HIP_LT=1) to avoid the gfx1201 page-fault bug (ROCm#6600).

Usage

llama-server -m qcoder-nl2sh-q4_k_m.gguf --temp 0

bash

whatisit setup --model ./qcoder-nl2sh-q4_k_m.gguf
whatisit doctor

Greedy decoding, output ≤64 tokens — answers with a single command. Non-command input still produces a harmless command (see robustness above).

Limitations

Single-turn, no shell state; English only.
One command per request, not scripts.
Quant-to-quant benchmark differences are within measurement noise.

barbarabhb/nl2sh-qwen25-coder-1.5b-GGUF

nl2sh-qwen25-coder-1.5b

Files

Robustness

Training setup

Usage

Limitations

barbarabhb/
nl2sh-qwen25-coder-1.5b-GGUF