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manojpanda
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Model Details
Model Provider
manojpanda
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
Evaluated on the full official test split of princeton-nlp/SWE-bench_Lite:
| Checkpoint | Solved / Total | Resolution Rate | 95% Wilson CI |
|---|---|---|---|
| Base Gemma 4 E4B | 25 / 300 | 8.33% | [5.71%, 12.01%] |
| Peta1 (Stage 3 ToR GRPO) | 101 / 300 | 33.67% | [28.56%, 39.19%] |
| Absolute Gain (Delta) | +76 tasks | +25.33pp | p = 5.23e-22 |
Paired Contingency Table (n = 300):
In a controlled 2-turn agentic evaluation across 20 repository tasks:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "manojpanda/peta1" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "Locate the bug in flask/wrappers.py where JSON decoding does not respect request encoding."}] prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False))import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "manojpanda/peta1" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "Locate the bug in flask/wrappers.py where JSON decoding does not respect request encoding."}] prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False))