Jarvis710
/

agentjudge-v2

@@ -1,209 +1,82 @@
 ---
 base_model: Qwen/Qwen3-8B
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen3-8B
-- lora
-- sft
-- transformers
-- trl
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.18.1

 ---
 base_model: Qwen/Qwen3-8B
 library_name: peft
+license: apache-2.0
 tags:
+  - agent-evaluation
+  - trajectory-judge
+  - qlora
+  - sft
+  - tool-calling
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# AgentJudge v2
+Fine-tuned Qwen3-8B (QLoRA) for evaluating AI agent trajectories. Scores tool selection, argument correctness, reasoning quality, output faithfulness, and step efficiency.
+Outperforms GPT-4o-as-judge on reasoning quality (0.633 vs 0.118), output faithfulness (0.264 vs 0.122), and step efficiency (0.633 vs 0.156) at 5x lower cost.
+## Results
+| Metric | AgentJudge v2 | GPT-4o | Gemini |
+|---|---|---|---|
+| ρ reasoning_quality | **0.633** | 0.118 | 0.730 |
+| ρ output_faithfulness | **0.264** | 0.122 | 0.507 |
+| ρ step_efficiency | **0.633** | 0.156 | 0.730 |
+| ρ tool_selection | -0.067 | 0.092 | 0.730 |
+| Cost (22 evals) | **$0.020** | $0.099 | $0.040 |
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-8B",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(base_model, "Jarvis710/agentjudge-v2")
+model.eval()
+```
+## Training
+| Parameter | Value |
+|---|---|
+| Base model | Qwen/Qwen3-8B |
+| Method | QLoRA (4-bit NF4) |
+| LoRA rank | 16 |
+| Learning rate | 5e-5 |
+| Epochs | 5 |
+| Training examples | 675 (229 real + 458 synthetic) |
+| Compute | Google Colab A100, ~40 min |
+## Dataset
+229 real agent trajectories from BFCL and HotPotQA, labeled using dual-judge consensus (GPT-4o + Gemini). 458 synthetic failures with 6 failure types, each with unique per-trajectory reasoning.
+## Limitations
+- Verdict prediction is weak (κ near zero) — dimension scores are reliable, overall verdicts are not
+- Trained on BFCL and HotPotQA only — may not generalize to other agent types
+- Inference latency ~64s on A100 due to Qwen3 thinking mode — vLLM deployment would reduce to ~2s
+## Links
+- GitHub: [github.com/YOUR_USERNAME/agent-judge](https://github.com/YOUR_USERNAME/agent-judge)
+- Dataset: [Jarvis710/agent-trajectory-eval-dataset](https://huggingface.co/datasets/Jarvis710/agent-trajectory-eval-dataset)