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Model: AuricErgeson/shellwhiz-7b
Source: Original Platform

.gitattributes

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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-7B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

Modelfile

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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."""

Qwen2.5-7B-Instruct.Q4_K_M.gguf

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README.md

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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.

chat_template.jinja

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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 %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 3584,
+    "initializer_range": 0.02,
+    "intermediate_size": 18944,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 28,
+    "model_type": "qwen2",
+    "num_attention_heads": 28,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 4,
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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,
+    "use_sliding_window": false,
+    "vocab_size": 152064
+}

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b4360dd6a184650ffc48056c2569bc603f896c5adfe94b10f1c79f809638aa5
+size 11422166

tokenizer_config.json

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+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+      "rstrip": false,
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+      "special": false
+    }
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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"
+}