jalva182
/

cli-agent-model

@@ -1,210 +1,151 @@
----
-base_model: unsloth/llama-3-8b-Instruct
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:unsloth/llama-3-8b-Instruct
-- grpo
-- lora
-- transformers
-- trl
-- unsloth
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
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 ## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
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 ## Training Details
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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 ## Evaluation
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 ## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ## Model Card Contact
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 ### Framework versions
 - PEFT 0.18.1

+---
+base_model: unsloth/llama-3-8b-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:unsloth/llama-3-8b-Instruct
+- grpo
+- lora
+- transformers
+- trl
+- unsloth
+license: apache-2.0
+language:
+- en
+---
+# CLI Agent — Llama 3 8B GRPO Fine-tune
+A LoRA adapter fine-tuned on Meta-Llama-3-8B-Instruct using GRPO (Group Relative Policy Optimization) to generate correct Linux shell commands from natural language task descriptions.
 ## Model Details
 ### Model Description
+- **Developed by:** Jose Alvarez, Carson Chiem, Prisha Bhattacharyya, Vishal Tyagi
+- **Model type:** Causal Language Model (LoRA adapter)
+- **Language(s) (NLP):** English
+- **License:** Meta Llama 3 Community License
+- **Finetuned from model:** unsloth/llama-3-8b-Instruct
+### Model Sources
+- **Repository:** https://github.com/Alvarez-Jose/unsloth-grpo-project
 ## Uses
 ### Direct Use
+Given a natural language description of a CLI task, the model outputs the correct shell command with no explanation, no markdown, and no backticks.
+Example:
+- Input: "Count the number of lines in /tmp/data/log.txt"
+- Output: `wc -l /tmp/data/log.txt`
 ### Out-of-Scope Use
+- Not intended for general conversation
+- Not suitable for tasks outside Linux CLI command generation
+- Should not be used for destructive or malicious shell commands
 ## Bias, Risks, and Limitations
+- Model may generate incorrect or harmful shell commands — always review before executing
+- Trained on a limited set of ~60 task types, may not generalize to all CLI scenarios
+- Performance degrades on complex multi-step tasks
 ## How to Get Started with the Model
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="jalva182/cli-agent-model",
+    max_seq_length=512,
+    load_in_4bit=True,
+)
+messages = [
+    {"role": "system", "content": "You are a CLI expert. Given a task, output exactly the shell commands required. No explanation, no markdown, no backticks."},
+    {"role": "user", "content": "Count the number of lines in /tmp/data/log.txt"},
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+outputs = model.generate(input_ids=inputs, max_new_tokens=64)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## Training Details
 ### Training Data
+60 validated CLI tasks covering file operations, text processing (grep, awk, sed), sorting, archives, system info, permissions, and environment variables. Each task includes setup commands, expected output, and a reward function for GRPO training.
+### Training Hyperparameters
+- **Training regime:** bf16 mixed precision
+- **Method:** GRPO (Group Relative Policy Optimization)
+- **Learning rate:** 3e-6 with linear scheduler
+- **Warmup ratio:** 0.1
+- **Batch size:** 2 (per device)
+- **Gradient accumulation steps:** 2
+- **Total steps:** 10000
+- **LoRA rank:** 32, alpha: 64
+- **KL coefficient:** 0.05
+- **Number of generations:** 4
+- **Max sequence length:** 512
+### Speeds, Sizes, Times
+- **Training time:** ~3h 13min
+- **Checkpoint size:** ~524MB (LoRA adapter only)
+- **Final train loss:** 0.0141
+- **Final reward:** 8.0/8.0 on easy tasks, ~6.0 average
 ## Evaluation
+### Metrics
+Reward function scoring 0-8 per task:
+- +5 for correct output match
+- +3 for command success with partial match
+- -2 for command failure or wrong output
 ### Results
+- **Best reward:** 8.0
+- **Average reward (final steps):** ~6.0
+- **Train loss:** 0.0141
 ## Environmental Impact
+- **Hardware Type:** H100 SXM 80GB
+- **Hours used:** ~3.5 hours
+- **Cloud Provider:** Vast.ai
+## Technical Specifications
+### Model Architecture
+- Base: Meta-Llama-3-8B-Instruct
+- Adapter: LoRA (rank=32, alpha=64, dropout=0.05)
+- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+### Software
+- unsloth 2026.3.3
+- trl 0.24.0
+- transformers 4.56.1
+- torch 2.6.0+cu124
+- PEFT 0.18.1
+## Model Card Authors
+Jose Alvarez
 ## Model Card Contact
+https://github.com/Alvarez-Jose/unsloth-grpo-project
 ### Framework versions
 - PEFT 0.18.1