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FROM Qwen2.5-7B-Instruct.Q4_K_M.gguf
TEMPLATE """{{- if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 1.5
PARAMETER min_p 0.1
SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- text-generation
- shell
- bash
- code
- qlora
- unsloth
language:
- en
datasets:
- AuricErgeson/text-to-shell-dataset
pipeline_tag: text-generation
---
# ShellWhiz-7B
A fine-tune of Qwen2.5-7B-Instruct that turns a plain-English request into a shell command. Type what you want to do, get back `find`, `grep`, `docker`, `git`, or whatever fits.
## Why this exists
I wanted something I could actually type "show me the 5 biggest files in this folder" into and get a working `du`/`sort`/`head` pipeline back, instead of half-remembering the flags myself. There are commercial tools that do this (Warp, some IDE plugins), but I couldn't find a small open model that just did the one thing well, so I built one.
## What it's good at
Trained on 697 natural-language-to-shell-command pairs covering:
- File and directory operations (`find`, `cp`, `mv`, `rm`, `chmod`, `du`)
- Text processing (`grep`, `sed`, `awk`, `sort`, `cut`)
- Git workflows
- Docker and docker-compose
- Process management (`ps`, `kill`, `systemctl`)
- Networking (`curl`, `ssh`, `scp`, `ping`)
- Archiving and package management (`tar`, `zip`, `apt`, `pip`, `npm`)
## Examples
These are from the actual post-training sanity check, not cherry-picked from the training set:
```
> find all python files modified in the last 24 hours
find <directory> -name '*.py' -mtime -1
> show me the 5 largest files in this directory
find . -type f -exec du -h {} + | sort -rh | head -5
> list all running docker containers
docker ps
```
The `<directory>` placeholder is intentional. The model was trained to use placeholders where a real path would depend on context, rather than guessing one.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "AuricErgeson/shellwhiz-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
system_msg = "You are a helpful assistant that converts natural language requests into precise shell commands. Respond with ONLY the shell command, no explanation."
messages = [
{"role": "system", "content": system_msg},
{"role": "user", "content": "find all files larger than 100MB"},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=100, temperature=0.1)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
```
A GGUF (q4_k_m) build is also available in this repo if you want to run it locally through Ollama or llama.cpp.
## Training details
- **Base model:** Qwen2.5-7B-Instruct
- **Method:** QLoRA (4-bit), rank 16, alpha 16, no dropout
- **Trainable parameters:** 40,370,176 of 7,655,986,688 (0.53 percent)
- **Hardware:** single T4 GPU, Google Colab free tier
- **Epochs:** 3, 264 total steps, effective batch size 8
- **Training time:** about 17 minutes
Loss dropped from 2.96 at step 10 to 0.18 by the end of training and flattened out around step 190, with no spikes or divergence:
| Step | Loss |
|------|------|
| 10 | 2.960 |
| 50 | 0.391 |
| 100 | 0.268 |
| 150 | 0.258 |
| 200 | 0.188 |
| 260 | 0.188 |
## Evaluation
I ran the model against 105 held-out prompts it never saw during training, phrased differently
from the training set on purpose to test generalization rather than recall. Each output was
judged by Claude against a known-correct reference command, allowing for different-but-equivalent
approaches (there's rarely only one right way to write a shell command).
| Verdict | Count | Percent |
|---------|-------|---------|
| Correct | 58 | 55.2% |
| Partial (right idea, has a bug) | 26 | 24.8% |
| Wrong | 21 | 20.0% |
Syntax validity (does `bash -n` parse it without error) came out at 100/105, or 95.2 percent.
The five syntax failures were almost all cases where the model left a bracketed placeholder like
`<filename>` or `<output_file>` in a spot where bash needs an actual token, which reads as a
formatting habit rather than the model not understanding the command it's building.
The wrong and partial cases cluster into a few recognizable patterns, worth knowing before you
rely on this for anything important:
- **Hallucinated flags.** A couple of failures invented flags that don't exist on the real tool
(`docker images --sort`, `pkill --exclude`). These would fail immediately with an error, so
at least they're not silently wrong.
- **Negation and inversion.** When a prompt asks for something to be turned *off* or a filter to
be the *inverse* of the obvious reading, the model sometimes gets the polarity backwards
(e.g. batch mode requested off, model turns it on).
- **Dropped constraints on multi-part requests.** Given an instruction with two or three
requirements stacked together (filter by host AND connection state, auto-remove AND port map),
the model sometimes satisfies one and quietly drops another.
- **Leftover placeholders when a real value was given.** A few outputs used `<image_name>:<tag>`
style placeholders even when the prompt spelled out a concrete value like `nginx:latest`.
None of this is surprising for 700 training examples on a 7B model, but it's worth knowing which
categories to double check rather than trusting blindly.
## Dataset
The training data is published separately at [AuricErgeson/text-to-shell-dataset](https://huggingface.co/datasets/AuricErgeson/text-to-shell-dataset), generated synthetically and deduplicated on instruction text.

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
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"torch_dtype": "float16",
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"rms_norm_eps": 1e-06,
"rope_parameters": {
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"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"unsloth_fixed": true,
"unsloth_version": "2026.7.2",
"use_cache": true,
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"vocab_size": 152064
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tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
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"errors": "replace",
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}