Add comprehensive model card with usage examples

README.md

@@ -1,14 +1,116 @@
 ---
-datasets:
-- open-vdb/glove-100-angular
-- rsh-raj/angular-cli-commits
-- rsh-raj/angular-commits
-- open-vdb/nytimes-16-angular
-- open-vdb/nytimes-256-angular
-- lone17/angular-steering-artifacts
-base_model:
-- Qwen/Qwen3.8-27B
-- Qwen/Qwen2.5-Omni-7B
+license: apache-2.0
+base_model: Qwen/Qwen3.8-27B
+tags:
+  - qwen
+  - finetune
+  - engineering
+  - code-generation
 language:
-- en
----
+  - en
+---
+
+# engineering_model
+
+Fine-tuned **Qwen3.8-27B** for engineering tasks: code generation, debugging, architecture design, and technical Q&A.
+
+## Base model
+[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
+
+## Datasets used
+- `open-vdb/glove-100-angular`
+- `open-vdb/nytimes-16-angular`
+- `open-vdb/nytimes-256-angular`
+- `rsh-raj/angular-cli-commits`
+- `rsh-raj/angular-commits`
+- `lone17/angular-steering-artifacts`
+
+## Usage
+
+### With transformers (full model)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "anmolthukral/engineering_model"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+prompt = "### User:\nWrite a Python function to detect cycles in a directed graph.\n### Assistant:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        repetition_penalty=1.1
+    )
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With 4-bit quantization (recommended for 27B)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "anmolthukral/engineering_model",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+### Chat template (Qwen format)
+```python
+messages = [
+    {"role": "user", "content": "Explain the difference between mutex and semaphore"},
+    {"role": "assistant", "content": "..."},
+    {"role": "user", "content": "Show me a C++ example"}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+# ... generate
+```
+
+## Hardware requirements
+
+| Precision | VRAM (single GPU) | Notes |
+|-----------|-------------------|-------|
+| bfloat16 | ~54 GB | 2×A100 80GB or 4×A10G |
+| 4-bit (NF4) | ~16 GB | 1×A10G / A100 40GB |
+| 8-bit | ~28 GB | 1×A100 40GB |
+
+## Limitations
+- Trained on Angular/engineering data — may be biased toward frontend/web patterns
+- 27B parameters requires significant compute for inference
+- Not evaluated on safety benchmarks — use with caution in production
+
+## Citation
+```bibtex
+@misc{engineering_model,
+  author = {Anmol Thukral},
+  title = {engineering_model: Qwen3.8-27B fine-tuned for engineering tasks},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/anmolthukral/engineering_model}}
+}
+```