Downloads · 30 days
0
termius-minimal-1.5
datasetter458/termius-minimal-1.5
termius-minimal-1.5 is a text generation model from datasetter458. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as mit.
A model with an objective of making as much as possible of the bash operations in a small and manageable size with good performance.<br To understand what the model is actually good at generating, check out the datase…
Access
Public
Updated May 22, 2026
Parameters
1.7B
7.5 GB on disk
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors5.5 GB · 100%
Parameter types
How the weights are stored.
I81.4B · 82%
Try a prompt
Base models
Model card
From the Hugging Face model README
💻 Termius minimal 1.5
A model with an objective of making as much as possible of the bash operations in a small and manageable size with good performance.<br> To understand what the model is actually good at generating, check out the datasets used to train him.<br>
👋 Getting started
pip install peft torch unsloth
Unsloth will already make patches for transformers, pyarrow, ..., and the
get_peft_model()
What this model is actually good at :
- Dedicated exactly for bash only operation : The model generates nothing other than bash commands, which shift its focus to only that specific task.
- Suitable for complicated use cases : Unlike the first version of him datasetter458/termius-minimal-1.0, this model is way more suitable for complicated commands (check the test samples).
- Near perfect command results : Near perfect answers for the commands he is mainly trained for (check the datasets).
- Blazingly fast performance : Due to it being bash specific, all the performance goes for only this task, which generate fast no matter how complex the command is (check the testing samples). <br>
🧪 Testing samples :
| prompt | answer |
|---|---|
| check if the file '/etc/nginx/nginx.conf' exists and print its line count | [[ -f "/etc/nginx/nginx.conf" ]] && wc -l < "/etc/nginx/nginx.conf" |
| check if the directory '/opt/myapp' exists and if not create it with permissions 755 | [[ -d "/opt/myapp" ]] || mkdir -p -m 755 "/opt/myapp" |
| check if port 443 is listening and log the result to /tmp/port_check.log | lsof -i :443 > /tmp/port_check.log |
| check if python3 is installed and print its version | [[ -e "/usr/bin/python3" ]] && python3 --version |
| find all .tmp files older than 7 days and delete them | find . -name "*.tmp" -mtime +7 -print | xargs -I {} rm -f {} |
| verify the file 'backup.tar.gz' exists then extract it to /opt/restore/ | if [[ -f "backup.tar.gz" ]]; then tar -xzf backup.tar.gz -C /opt/restore/; fi |
| check if a user 'guest' exists and if so lock their account and kill their processes | if id "guest" &>/dev/null; then passwd -l guest && pkill -u guest; fi |
| count how many failed SSH login attempts are in /var/log/auth.log | grep -c "Failed" /var/log/auth.log |
| check if a git repo has uncommitted changes and stash them before pulling | if git status --porcelain | grep -q .; then git stash push -m "auto-stash before pull"; fi |
| delete all files in /tmp older than 1 day but keep directories | find /tmp -type f -mtime +1 -print0 | xargs -0 rm |
this model was trained and tested using unsloth, 8bit quantization and a Tesla T4.
Fine-tuning configs:
| Parameter | Value |
|---|---|
| Framework | Unsloth(DoRA + rsLoRA) |
| Num GPUs used | 1 |
| Num examples | 9,351 |
| Num Epochs | 3 |
| Total steps | 879 |
| Batch size per device | 4 |
| Gradient accumulation steps | 8 |
| Data Parallel GPUs | 1 |
| Total batch size (4 x 8 x 1) | 32 |
| Trainable parameters | 35,438,592 of 1,756,013,568 (2.02% trained) |
This is for the 1st dataset only, the second dataset had : <br>
| Parameter | Value |
|---|---|
| Num examples | 4,912 |
| Num Epochs | 2 |
📊 Datasets used :
- datasetter458/diverse-bash-dataset-extended : this is the biggest dataset the model is trained on, containing most of the standard bash command set.
- datasetter458/diverse-bash-dataset : this is the second biggest dataset the model is trained on, with the same objective and structure as the biggest one.
👩💻 Code example :
Which is mostly the same code used for Qwen3-1.7B inference.<br>
Important : When testing or fine-tuning this model, make sure it is loaded in 8bits(or 16bits in '16bit_version_' directory), using unsloth's FastLanguageModel<br>
conversation = [
{"role" : "system", "content" : "you are an AI assistant specialized in writing bash code."},
{"role" : "user", "content" : "how to list the content of the directory 'abc/'"}
]
tokens_str = tokenizer.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=False
)
model_inputs = tokenizer(tokens_str, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = output[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print(content)
Citation :
Use this citation ❤🙏
@misc{termius-minimal-1.5,
author = {datasetter458},
title = {termius-minimal-1.5},
year = {2026},
url = {https://huggingface.co/datasetter458/termius-minimal-1.5}
}
- Task
- Text Generation
- Library
- peft
- Type
- qwen3
- License
- mit
- Languages
- en
- Created
- May 21, 2026
- Updated
- May 22, 2026