Update model card

README.md

@@ -13,7 +13,7 @@ tags:
   - granite
   - qiskit
 model-index:
-  - name: granite-8b-qiskit
+  - name: granite-8b-rc-0.10
     results:
       - task:
           type: text-generation
@@ -23,7 +23,7 @@ model-index:
         metrics:
           - name: pass@1
             type: pass@1
-            value: 45.69
+            value: 34.43
             verified: false
       - task:
           type: text-generation
@@ -33,21 +33,21 @@ model-index:
         metrics:
           - name: pass@1
             type: pass@1
-            value: 58.53
+            value: 59.14
             verified: false
 ---
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5e6f94a5d4cd9779932a7610/rkvd1vRSwF0pIpcHAd5Ai.png)
 
-# granite-8b-qiskit
+# granite-8b-rc-0.10
 
 ## Model Summary
 
-**granite-8b-qiskit** is a 8B parameter model extend pretrained and fine tuned on top of [granite-8b-code-base](https://huggingface.co/ibm-granite/granite-8b-code-base-4k) using Qiskit code and instruction data to improve capabilities at writing high-quality and non-deprecated Qiskit code. We used only data with the following licenses: Apache 2.0, MIT, the Unlicense, Mulan PSL Version 2, BSD-2, BSD-3, and Creative Commons Attribution 4.0. 
+**granite-8b-rc-0.10** is a 8B parameter model extend pretrained and fine tuned on top of [granite-8b-code-base](https://huggingface.co/ibm-granite/granite-8b-code-base-4k) using Qiskit code and instruction data to improve capabilities at writing high-quality and non-deprecated Qiskit code. We used only data with the following licenses: Apache 2.0, MIT, the Unlicense, Mulan PSL Version 2, BSD-2, BSD-3, and Creative Commons Attribution 4.0. 
 
 - **Developers:** IBM Quantum & IBM Research
 - **Related Papers:** [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://arxiv.org/abs/2405.19495) and [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://arxiv.org/abs/2406.14712)
-- **Release Date**: November 12th, 2024
+- **Release Date**: February 14th, 2025
 - **License:** apache-2.0
 
 ## Usage
@@ -58,13 +58,13 @@ This model is designed for generating quantum computing code using Qiskit. Both
 
 ### Generation
 
-This is a simple example of how to use **granite-8b-qiskit** model.
+This is a simple example of how to use **granite-8b-rc-0.10** model.
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # or "cpu"
-model_path = "qiskit/granite-8b-qiskit"
+model_path = "qiskit/granite-8b-rc-0.10"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 # drop device_map if running on CPU
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
@@ -96,8 +96,8 @@ for i in output:
 
 ## Infrastructure
 
-We trained **granite-8b-qiskit** using IBM's super computing cluster (Vela) using NVIDIA A100 GPUs.
+We trained **granite-8b-rc-0.10** using IBM's super computing cluster (Vela) using NVIDIA A100 GPUs.
 
 ## Ethical Considerations and Limitations
 
-The use of Large Language Models involves risks and ethical considerations people must be aware of. Regarding code generation, caution is urged against complete reliance on specific code models for crucial decisions or impactful information as the generated code is not guaranteed to work as intended. **granite-8b-qiskit** model is not the exception in this regard. Even though this model is suited for multiple code-related tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying source code verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use **granite-8b-qiskit** model with ethical intentions and in a responsible way.
+The use of Large Language Models involves risks and ethical considerations people must be aware of. Regarding code generation, caution is urged against complete reliance on specific code models for crucial decisions or impactful information as the generated code is not guaranteed to work as intended. **granite-8b-rc-0.10** model is not the exception in this regard. Even though this model is suited for multiple code-related tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying source code verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use **granite-8b-rc-0.10** model with ethical intentions and in a responsible way.