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README.md

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----
-license: mit
----
+---
+base_model: microsoft/phi-3-mini-128k-instruct
+library_name: peft
+model_name: phi3-nl2bash-lora
+tags:
+- base_model:adapter:microsoft/phi-3-mini-128k-instruct
+- lora
+- sft
+- nl2bash
+- transformers
+- trl
+license: mit
+pipeline_tag: text-generation
+---
+
+# phi3-nl2bash-lora
+This repository contains **LoRA adapter weights** fine-tuned on the
+[`jiacheng-ye/nl2bash`](https://huggingface.co/datasets/jiacheng-ye/nl2bash)
+dataset to convert **natural language instructions into Linux bash commands**.
+
+> ⚠️ This repo contains **LoRA adapters only**, not the base model.
+> You must load it on top of `microsoft/phi-3-mini-128k-instruct`.
+
+---
+
+## Intended behavior
+**Input (natural language):**
+`Do a dry run of renaming file extension '.andnav' to '.tile'`
+
+**Output (bash only):**
+```bash
+find . -name "*.andnav" | rename -vn "s/\.andnav$/.tile/"
+```
+
+The model is trained to output only the bash command, with no explanation.
+
+## Quick start
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+base_model_id = "microsoft/phi-3-mini-128k-instruct"
+lora_model_id = "ayertiam/phi3-nl2bash-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+model = PeftModel.from_pretrained(base_model, lora_model_id)
+
+prompt = "List all .txt files recursively in the current directory"
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    do_sample=False
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+## Training details
+- Base model: microsoft/phi-3-mini-128k-instruct
+- Fine-tuning method: LoRA (PEFT)
+- Trainer: TRL SFTTrainer
+- Dataset: jiacheng-ye/nl2bash
+- Output style: bash command only
+
+
+### Framework versions
+
+- PEFT 0.18.0
+- TRL: 0.26.1
+- Transformers: 4.57.3
+- Pytorch: 2.9.0+cu126
+- Datasets: 4.4.1
+- Tokenizers: 0.22.1
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/phi-3-mini-128k-instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "v_proj",
+    "k_proj",
+    "up_proj",
+    "gate_proj",
+    "q_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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added_tokens.json

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+  "<|user|>": 32010
+}

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'user' %}{{'<|user|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
+' + message['content'] + '<|end|>
+'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
+' }}{% else %}{{ eos_token }}{% endif %}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+  "unk_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 499723

tokenizer_config.json

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+  },
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+  "legacy": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}