This repository contains GGUF releases of Chris Linux 124M, a 124,475,904-parameter GPT-2-style language model specialized for Linux terminal assistance and natural-language-to-command generation.
All GGUF files in this repository are derived from the same published model checkpoint:
Important: never automatically execute commands generated by this model. GGUF quantization changes the numerical representation of the weights and may also change individual outputs. Every quantized variant should be evaluated independently before use in an application.
Available files
File
Representation
Recommended use
Chris-Linux-124M-F16.gguf
F16
reference GGUF / highest-fidelity GGUF release
Chris-Linux-124M-Q8_0.gguf
Q8_0
high-quality local CPU inference; recommended default quantization
Chris-Linux-124M-Q4_0.gguf
Q4_0
smaller footprint / constrained local environments
The F16 file is the reference GGUF conversion. Q8_0 and Q4_0 are post-conversion quantizations of that F16 representation.
The model was specialized in stages. Linux continued pretraining improved its domain distribution, but CPT alone did not reliably turn natural-language instructions into precise shell commands. A larger heterogeneous instruction dataset also caused frequent confusion between nearby tools, flags, and argument patterns.
Core SFT v1 therefore follows the principle:
diverse inputs, canonical outputs
Examples:
text
1show disk usage
2→ df -h
34show listening TCP ports
5→ ss -lntp
67show active TCP connections
8→ ss -tnp
910what process is using port 8080
11→ sudo lsof -i :8080
1213show process with PID 8080
14→ ps -p 8080
1516show traffic control configuration
17→ tc qdisc show
1819find files larger than one gigabyte
20→ find . -type f -size +1G
The Core SFT dataset contains 3,335 training examples, 643 validation examples, and 106 intents across 15 Linux-oriented categories.
Reference model evaluation
Before quantization, the Core SFT epoch-08 checkpoint obtained the following result on the Core command benchmark:
Metric
Reference result
Command examples
271
Exact matches
181
Exact match
66.7897%
Extra unsafe suggestions
0
Extra unsafe rate
0.0%
The 307-item Core benchmark contains 271 command examples, 28 diagnostic examples, and 8 safety examples. The exact-match result above refers specifically to the 271 command examples.
Important quantization note
The 66.79% figure belongs to the source Core SFT checkpoint/reference model evaluation. Do not automatically assume that F16, Q8_0, and Q4_0 all reproduce exactly the same score.
Quantization can change token rankings near decision boundaries. For publication-quality comparisons, run the same benchmark separately on each GGUF variant.
Prompt format
Use the same instruction format as the Transformers model:
text
1User: <request>
2Assistant:
Example:
text
1User: show disk usage
2Assistant:
Expected answer style:
df -h
The model is designed to generate short Linux-oriented answers rather than long conversational responses.
Running with Chris-Llama
Chris-Llama is the low-level inference runtime developed alongside the project.
The repository should also contain SHA256SUMS.txt with checksums for the actual distributed GGUF files.
Verify a downloaded file with:
sha256sum Chris-Linux-124M-Q8_0.gguf
or verify all published files from the repository directory:
sha256sum -c SHA256SUMS.txt
Recommended variant
F16
Use F16 when:
validating conversion quality;
comparing against the Transformers/SafeTensors release;
measuring quantization degradation;
disk/RAM footprint is not the main constraint.
Q8_0
Q8_0 is the recommended default for this 124M model when local resources allow it. At this small parameter count, the memory savings of aggressive 4-bit quantization are less compelling than they are for multi-billion-parameter models, while preserving output stability is valuable for shell-command generation.
Q4_0
Use Q4_0 when minimizing model footprint matters more than preserving the closest possible behavior to the reference model. Validate command quality independently before integrating it into a terminal workflow.
Intended use
The GGUF release is intended for:
local/offline Linux command-assistant experiments;
CPU inference;
lightweight terminal integrations;
Chris-Llama runtime development;
GGUF loader and quantization testing;
benchmarking small specialized language models;
educational systems and inference research.
Limitations
Chris Linux 124M is intentionally small. It does not have the broad reasoning or coding capacity of modern multi-billion-parameter assistants.
Possible failure modes include:
choosing the wrong Linux utility;
producing a valid utility with incorrect flags;
confusing a port number with a PID or another numeric argument;
composing mutually contradictory predicates;
hallucinating Linux paths, files, flags, or tools;
producing non-portable commands;
failing on requests outside the curated Core intent space;
failing to recognize a destructive operation when it is phrased in an unfamiliar way.
Quantization may introduce additional output differences.
Safety
Do not connect raw model output directly to command execution.
The model may generate commands that modify or destroy data. Any application built around this model should place a deterministic safety layer between generation and execution.
At minimum, consider blocking or requiring explicit confirmation for command families involving:
rm with recursive/root-like targets;
filesystem formatting tools such as mkfs;
wipefs;
raw block-device writes with dd;
partitioning tools;
recursive permission/ownership changes;
shutdown/reboot operations;
package removal;
service changes;
privileged operations through sudo.
A model-generated command being syntactically valid does not make it operationally safe.
Training-data provenance and licensing
The model combines several training stages:
FineWeb-Edu-based base pretraining;
Linux continued pretraining from multiple upstream documentation projects;
manually curated Core SFT v1 examples.
The Linux documentation sources have heterogeneous upstream licenses. For that reason, this repository conservatively uses:
license: other
Users should review the provenance and licenses of upstream sources before redistribution or commercial use of derived weights.
Until the Linux-specialization paper has a stable public identifier, the project can be cited by its repository:
bibtex
1@misc{silva2026chrislinux,
2 author = {Christian Rafael de Souza Silva},
3 title = {Chris Linux 124M: A GPT-2-Style Language Model Specialized for Linux Terminal Assistance},
4 year = {2026},
5 howpublished = {GitHub repository},
6 url = {https://github.com/christianrss/chris-gpt-2}
7}
Disclaimer
Chris Linux 124M is an experimental research model. The author and contributors do not guarantee that generated shell commands are correct, safe, portable, or appropriate for any specific system.
