jalva182
/

cli-agent-model-gpu1

@@ -1,9 +1,9 @@
 ---
-base_model: unsloth/llama-3-8b-instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/llama-3-8b-instruct-bnb-4bit
 - grpo
 - lora
 - transformers
@@ -11,200 +11,150 @@ tags:
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
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 ## How to Get Started with the Model
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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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 ### 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
 - unsloth
 ---
+# CLI Agent — Llama 3 8B GRPO Fine-tune (GPU 1 / lr=5e-6)
+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. This is the GPU 1 run trained at lr=5e-6. See also [jalva182/cli-agent-model](https://huggingface.co/jalva182/cli-agent-model) for the GPU 0 run at lr=3e-6.
 ## 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-gpu1",
+    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:** 5e-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:** ~4h 7min
+- **Checkpoint size:** ~524MB (LoRA adapter only)
+- **Final train loss:** 0.0188
+- **Final reward:** 8.0/8.0 on final steps
 ## 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:** 0.0188
+## Comparison with GPU 0 Run
+| | GPU 0 (cli-agent-model) | GPU 1 (cli-agent-model-gpu1) |
+|---|---|---|
+| Learning rate | 3e-6 | 5e-6 |
+| Train loss | 0.0141 | 0.0188 |
+| Final reward | 8.0 | 8.0 |
+| Runtime | 3h 13min | 4h 7min |
+| Recommendation | ✅ Primary | Secondary |
+GPU 0 achieved lower train loss and is recommended as the primary model.
 ## Environmental Impact
+- **Hardware Type:** H100 SXM 80GB
+- **Hours used:** ~4.5 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