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README.md

@@ -1,22 +1,120 @@
----
-base_model: deepseek-ai/deepseek-coder-6.7b-instruct
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded  model
-
-- **Developed by:** Ghaythfd
-- **License:** apache-2.0
-- **Finetuned from model :** deepseek-ai/deepseek-coder-6.7b-instruct
-
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+license: apache-2.0
+base_model: deepseek-ai/deepseek-coder-6.7b-instruct
+tags:
+  - gherkin
+  - bdd
+  - test-automation
+  - cucumber
+  - lora
+  - peft
+  - deepseek
+language:
+  - en
+pipeline_tag: text-generation
+library_name: peft
+datasets:
+  - Ghaythfd/gherkin-scenarios
+---
+
+# Gherkin Scenario Generator (DeepSeek Coder LoRA)
+
+A fine-tuned LoRA adapter for generating Gherkin BDD test scenarios, built on top of DeepSeek Coder 6.7B Instruct.
+
+## Model Description
+
+This model generates Gherkin/Cucumber test scenarios for data management systems. It was fine-tuned on real-world BDD test cases covering:
+
+- Data import/export (CSV, JSON, Excel)
+- REST and SOAP API testing
+- UI navigation and search
+- Job scheduling and reporting
+- IBOR and financial data operations
+
+## Usage
+
+### With Unsloth (Recommended)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="Ghaythfd/gherkin-deepseek-lora",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+prompt = "### Instruction:\nWrite a Gherkin scenario for testing CSV file import\n\n### Response:\n"
+inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.5,
+    top_p=0.9,
+    repetition_penalty=1.15,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With PEFT/Transformers
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "deepseek-ai/deepseek-coder-6.7b-instruct",
+    device_map="auto",
+    load_in_4bit=True,
+)
+model = PeftModel.from_pretrained(base_model, "Ghaythfd/gherkin-deepseek-lora")
+tokenizer = AutoTokenizer.from_pretrained("Ghaythfd/gherkin-deepseek-lora")
+```
+
+## Prompt Format
+
+```
+### Instruction:
+{your request here}
+
+### Response:
+```
+
+## Example Output
+
+**Prompt:** "Write a Gherkin scenario for testing CSV file import"
+
+**Output:**
+```gherkin
+Scenario Outline: Testing CSV file import
+    Given I am logged as TAV_standard.user on <screen_name>
+    And I go to detail screen <detail_screen> of <object_type>
+    When I select the tab <tab>
+    Then The fields <fields> should be displayed with values <values>
+    
+    Examples:
+      | screen_name   | object_type | detail_screen | tab      | fields | values |
+      | My Securities | Equity      | Detail        | Overview | Name   | Test   |
+```
+
+## Training Details
+
+- **Base Model:** deepseek-ai/deepseek-coder-6.7b-instruct
+- **Method:** LoRA (Low-Rank Adaptation)
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 16
+- **Training Data:** 726 examples from Gherkin feature files
+- **Epochs:** 1
+- **Framework:** Unsloth + TRL
+
+## Limitations
+
+- Generates scenarios in the style of the training data (data management domain)
+- May hallucinate specific field names or values
+- Works best for scenarios similar to the training examples
+
+## License
+
+Apache 2.0