Upload nishka-gkc-phi3-merged - GKC merged model with modeling_phi3.py for RunPod

README.md

@@ -0,0 +1,50 @@
+---
+license: mit
+language:
+- en
+tags:
+- pql
+- compliance
+- governance
+- phi-3
+base_model: microsoft/Phi-3-mini-4k-instruct
+---
+
+# NISHKA GKC
+
+Governance Knowledge Corpus model trained on 1.12M tokens of regulatory content across 15 compliance frameworks.
+
+## Model Details
+
+- **Base Model**: microsoft/Phi-3-mini-4k-instruct
+- **Architecture**: Phi-3 (3.8B parameters)
+- **Training**: LoRA adapter merged into base model
+- **Format**: Full model weights (no adapter needed)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "openpql/nishka-gkc",
+    device_map="auto",
+    torch_dtype="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("openpql/nishka-gkc")
+
+# Generate
+inputs = tokenizer("Your prompt here", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Deployment
+
+This model is ready for deployment with vLLM, TGI, or other inference servers.
+
+```bash
+# vLLM example
+vllm serve openpql/nishka-gkc --dtype float16
+```

config.json

@@ -29,7 +29,6 @@
   "rope_theta": 10000.0,
   "sliding_window": 2047,
   "tie_word_embeddings": false,
-  "transformers_version": "4.57.3",
   "use_cache": true,
   "vocab_size": 32064
 }