Update README.md
Browse files
README.md
CHANGED
|
@@ -1,14 +1,12 @@
|
|
| 1 |
lora-llama2-finetuned
|
| 2 |
-
=======================================
|
| 3 |
|
| 4 |
This model is a fine-tuned instruction-following Large Language Model (LLM) specialized in generating, analyzing, and explaining Dockerfiles. It was adapted from the Llama 2 7B Chat base model using the QLoRA efficient fine-tuning method.
|
| 5 |
|
| 6 |
-
|
| 7 |
1. Model Description
|
| 8 |
-
=======================================
|
| 9 |
|
| 10 |
Model ID:
|
| 11 |
-
[
|
| 12 |
|
| 13 |
Base Model:
|
| 14 |
* Architecture: Llama 2 7 Billion Parameters
|
|
@@ -26,9 +24,8 @@ Critical Note on Scale: This model was fine-tuned on a very limited dataset (20
|
|
| 26 |
* Security: Generated Dockerfiles may contain insecure commands, outdated dependencies, or other security vulnerabilities. Always review and validate generated code before use in a production environment.
|
| 27 |
* Bias: The model inherits potential biases from its base model, Llama 2.
|
| 28 |
|
| 29 |
-
=======================================
|
| 30 |
2. Training Details
|
| 31 |
-
|
| 32 |
|
| 33 |
The model was fine-tuned using the QLoRA (Quantized Low-Rank Adaptation) technique, which loads the base model in 4-bit precision and only trains a small set of adapter weights.
|
| 34 |
|
|
@@ -41,9 +38,9 @@ Configuration:
|
|
| 41 |
* Training Epochs: 3 (Number of passes over the entire dataset.)
|
| 42 |
* Final Validation Loss: 1.706886 (Indicates the loss on the small test set.)
|
| 43 |
|
| 44 |
-
|
| 45 |
3. Training Data
|
| 46 |
-
|
| 47 |
|
| 48 |
The model was trained on a custom instruction-tuning dataset designed to teach the model to follow specific prompts related to Dockerfiles.
|
| 49 |
|
|
@@ -65,9 +62,8 @@ The inference pipeline must use the following template:
|
|
| 65 |
### Response:
|
| 66 |
[The model's generated Dockerfile, explanation, or analysis]
|
| 67 |
|
| 68 |
-
=======================================
|
| 69 |
4. How to Use (Inference)
|
| 70 |
-
|
| 71 |
|
| 72 |
Since this is a QLoRA adapter, you must load the base model (NousResearch/Llama-2-7b-chat-hf) and then merge the adapter weights from this repository.
|
| 73 |
|
|
|
|
| 1 |
lora-llama2-finetuned
|
|
|
|
| 2 |
|
| 3 |
This model is a fine-tuned instruction-following Large Language Model (LLM) specialized in generating, analyzing, and explaining Dockerfiles. It was adapted from the Llama 2 7B Chat base model using the QLoRA efficient fine-tuning method.
|
| 4 |
|
| 5 |
+
|
| 6 |
1. Model Description
|
|
|
|
| 7 |
|
| 8 |
Model ID:
|
| 9 |
+
[Arsh014/lora-llama2-finetuned]
|
| 10 |
|
| 11 |
Base Model:
|
| 12 |
* Architecture: Llama 2 7 Billion Parameters
|
|
|
|
| 24 |
* Security: Generated Dockerfiles may contain insecure commands, outdated dependencies, or other security vulnerabilities. Always review and validate generated code before use in a production environment.
|
| 25 |
* Bias: The model inherits potential biases from its base model, Llama 2.
|
| 26 |
|
|
|
|
| 27 |
2. Training Details
|
| 28 |
+
|
| 29 |
|
| 30 |
The model was fine-tuned using the QLoRA (Quantized Low-Rank Adaptation) technique, which loads the base model in 4-bit precision and only trains a small set of adapter weights.
|
| 31 |
|
|
|
|
| 38 |
* Training Epochs: 3 (Number of passes over the entire dataset.)
|
| 39 |
* Final Validation Loss: 1.706886 (Indicates the loss on the small test set.)
|
| 40 |
|
| 41 |
+
|
| 42 |
3. Training Data
|
| 43 |
+
|
| 44 |
|
| 45 |
The model was trained on a custom instruction-tuning dataset designed to teach the model to follow specific prompts related to Dockerfiles.
|
| 46 |
|
|
|
|
| 62 |
### Response:
|
| 63 |
[The model's generated Dockerfile, explanation, or analysis]
|
| 64 |
|
|
|
|
| 65 |
4. How to Use (Inference)
|
| 66 |
+
|
| 67 |
|
| 68 |
Since this is a QLoRA adapter, you must load the base model (NousResearch/Llama-2-7b-chat-hf) and then merge the adapter weights from this repository.
|
| 69 |
|