Falah
/

my-qlora-mistral7b-instruct

@@ -1,206 +1,97 @@
 ---
-base_model: mistralai/Mistral-7B-Instruct-v0.2
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2
-- lora
 ---
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-### Framework versions
-- PEFT 0.17.0

 ---
 tags:
+- text-generation
+- transformers
+- peft
+- qlora
+- bitsandbytes
+- mistral
+- mistral-7b
+- fine-tune
+license: apache-2.0
 ---
+# my-qlora-mistral7b-instruct
+This is a **QLoRA fine-tuned** version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model.
+It was fine-tuned using **Low-Rank Adaptation (LoRA)** in 4-bit precision for efficiency on consumer GPUs.
+## 🚀 Model Details
+- **Base model**: mistralai/Mistral-7B-Instruct-v0.2
+- **Fine-tuning method**: QLoRA with PEFT
+- **Quantization**: 4-bit (bitsandbytes)
+- **Task**: Instruction following / conversational AI
+- **Dataset**: Custom instruction-response pairs
+- **Training environment**: Google Colab Pro (T4 / A100 GPU)
+## 📦 How to Use
+```python
+# First, make sure you have the necessary libraries installed:
+# pip install transformers peft bitsandbytes accelerate
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
+from peft import PeftModel
+from accelerate import infer_auto_device_map, dispatch_model
+fine_tuned_model_id = "Falah/my-qlora-mistral7b-instruct"
+base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model_id)
+print("Loading base model with quantization...")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map=None, # Load to CPU initially
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+print("Loading PEFT adapter onto the base model...")
+model = PeftModel.from_pretrained(base_model, fine_tuned_model_id)
+print("Dispatching model to devices...")
+device_map = infer_auto_device_map(model, dtype=torch.float16)
+model = dispatch_model(model, device_map=device_map)
+# Ensure the model is in evaluation mode
+model.eval()
+print("Creating text generation pipeline...")
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+# Define a sample user prompt
+user_prompt = "Write a short story about a robot learning to love."
+# Format the prompt
+formatted_prompt = f"[INST] {user_prompt} [/INST]"
+# Generate text
+outputs = generator(
+    formatted_prompt,
+    max_new_tokens=200,
+    num_return_sequences=1,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95,
+)
+# Print the generated text
+for i, output in enumerate(outputs):
+    print(f"Generated Output {i+1}:\n{output['generated_text']}")
+```