Model card upd

README.md

@@ -1,21 +1,138 @@
 ---
+license: apache-2.0
 base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
-license: apache-2.0
+- lora
+- fastapi
+- code-assistant
 language:
 - en
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
-# Uploaded finetuned  model
+# Qwen2.5-Coder-7B-FastAPI-LoRA
+
+A LoRA fine-tune of **Qwen2.5-Coder-7B-Instruct** specialized as a **FastAPI documentation assistant**. The model is trained to answer questions, generate code, and explain concepts related to the FastAPI framework, covering everything from basic routing to advanced topics like security and testing.
 
-- **Developed by:** LadiesMan69
+## Model Details
+
+- **Developed by:** [LadiesMan69](https://huggingface.co/LadiesMan69)
 - **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
+- **Base model:** [unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit)
+- **Model type:** Causal decoder-only language model (Qwen2 architecture)
+- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
+- **Language:** English
+- **Trained with:** [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL — 2x faster training
+
+## Motivation
+
+General-purpose code models are often imprecise or outdated when it comes to framework-specific APIs. This model was fine-tuned on a curated dataset of FastAPI-focused instruction/response pairs to produce a lightweight, deployable assistant that gives accurate, idiomatic answers for building and debugging FastAPI applications.
+
+## Training Data
+
+The fine-tuning dataset was built specifically for this task using the **ChatML** format and organized into topic categories, including:
+
+- **Tutorial** — core concepts: path/query parameters, request bodies, response models, dependency injection
+- **Advanced** — background tasks, middleware, WebSockets, custom exception handlers, lifespan events
+- **Security** — OAuth2/JWT authentication, password hashing, CORS, rate limiting
+- **Testing** — `TestClient` usage, pytest fixtures, mocking dependencies, async test patterns
+
+Examples were generated in batches per category to ensure balanced topic coverage and consistent formatting across the dataset.
+
+## Intended Use
+
+- Answering questions about FastAPI concepts, patterns, and best practices
+- Generating FastAPI route handlers, Pydantic models, and dependency-injected services
+- Explaining and debugging FastAPI-related code snippets
+- Acting as an in-editor or chat-based documentation assistant for developers working with FastAPI
+
+## How to Use
+
+### With `transformers`
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a helpful FastAPI documentation assistant."},
+    {"role": "user", "content": "How do I add JWT-based authentication to a FastAPI route?"},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+### With `unsloth`
+
+```python
+from unsloth import FastModel
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name="LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA",
+    max_seq_length=2048,
+)
+```
+
+### With `vLLM`
+
+```bash
+pip install vllm
+vllm serve "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA"
+```
+
+```bash
+curl -X POST "http://localhost:8000/v1/chat/completions" \
+  -H "Content-Type: application/json" \
+  --data '{
+    "model": "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA",
+    "messages": [
+      {"role": "user", "content": "Show me a minimal FastAPI app with a health check endpoint."}
+    ]
+  }'
+```
+
+## Prompt Format
+
+This model uses the ChatML-style chat template built into the tokenizer (`apply_chat_template`). For best results, include a system message establishing the assistant's role as a FastAPI expert, followed by the user's question.
+
+## Limitations
+
+- Focused specifically on FastAPI; general coding ability outside this domain is inherited from the base model and not specifically enhanced.
+- As with any LLM, generated code should be reviewed and tested before use in production.
+- May not reflect the very latest FastAPI releases if they postdate the training data.
+
+## Training Procedure
+
+Fine-tuned using LoRA adapters on top of the 4-bit quantized `unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit` base model, leveraging Unsloth's optimized training kernels for faster, memory-efficient fine-tuning.
+
+## Model Tree
+
+- Base: [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
+- → [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
+- → [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+- → [unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit) (quantized)
+- → **LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA** (this model, LoRA fine-tune)
+
+## Acknowledgements
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[![Made with Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)