update context length

README.md

@@ -19,7 +19,7 @@ tags:
 - code
 - granite
 model-index:
-- name: granite-34b-code-base
+- name: granite-34b-code-base-8k
   results:
   - task:
       type: text-generation
@@ -225,10 +225,10 @@ model-index:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd5057674cdb524450093d/1hzxoPwqkBJXshKVVe6_9.png)
 
-# Granite-34B-Code-Base
+# Granite-34B-Code-Base-8K
 
 ## Model Summary
-**Granite-34B-Code-Base** is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing, etc.). It is trained using depth upscaling from *Granite-20B-Code-Base* model with a two-phase training strategy. In phase 1, our model is trained on 3 trillion tokens (1.4 trillion tokens after depth up scaling). In phase 2, our model is trained on 500 billion tokens with a carefully designed mixture of high-quality data from code and natural language domains to improve the models’ ability to reason and follow instructions.
+**Granite-34B-Code-Base-8K** is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing, etc.). It is trained using depth upscaling from *Granite-20B-Code-Base* model with a two-phase training strategy. In phase 1, our model is trained on 3 trillion tokens (1.4 trillion tokens after depth up scaling). In phase 2, our model is trained on 500 billion tokens with a carefully designed mixture of high-quality data from code and natural language domains to improve the models’ ability to reason and follow instructions.
 
 - **Developers:** IBM Research
 - **GitHub Repository:** [ibm-granite/granite-code-models](https://github.com/ibm-granite/granite-code-models)
@@ -241,13 +241,13 @@ model-index:
 Prominent enterprise use cases of LLMs in software engineering productivity include code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation, and more. All Granite Code Base models, including the **34B parameter model**, are able to handle these tasks as they were trained on a large amount of code data from 116 programming languages. 
 
 ### Generation
-This is a simple example of how to use **Granite-34B-Code-Base** model.
+This is a simple example of how to use **Granite-34B-Code-Base-8K** model.
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # or "cpu"
-model_path = "ibm-granite/granite-34b-code-base"
+model_path = "ibm-granite/granite-34b-code-base-8k"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 # drop device_map if running on CPU
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
@@ -278,4 +278,4 @@ for i in output:
 We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of 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-34B-Code-Base** 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-34B-Code-Base** 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-34B-Code-Base-8K** 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-34B-Code-Base-8K** model with ethical intentions and in a responsible way.