Update MODEL_CARD - Feedback links/format (#153)

Fix broken links to README.md
Standardize feedback section for MODEL_CARD.md for Llama 2, 3, 3.1, and 3.2
Remove redundant wording "Where to send questions or comments about the model"

5 files changed, 7 insertions, 7 deletions

diff --git a/models/llama2/MODEL_CARD.md b/models/llama2/MODEL_CARD.md
index ccf8b26..76771a1 100644
--- a/models/llama2/MODEL_CARD.md
+++ b/models/llama2/MODEL_CARD.md
@@ -28,7 +28,7 @@ Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x
 
 **Research Paper** More information can be found in the paper "Llama-2: Open Foundation and Fine-tuned Chat Models", available at https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/.
 
-**Where to send questions or comments about the model** Instructions on how to provide feedback or comments on the model can be found in the model [README](README.md).
+**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md).
 
 # **Intended Use**
 **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
diff --git a/models/llama3/MODEL_CARD.md b/models/llama3/MODEL_CARD.md
index a532fa0..6d468da 100644
--- a/models/llama3/MODEL_CARD.md
+++ b/models/llama3/MODEL_CARD.md
@@ -67,7 +67,7 @@ Note: Token counts refer to pretraining data only.
 
 **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
 
-Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
+**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
 
 
 ## Intended Use
diff --git a/models/llama3_1/MODEL_CARD.md b/models/llama3_1/MODEL_CARD.md
index 0556ab0..97c39fe 100644
--- a/models/llama3_1/MODEL_CARD.md
+++ b/models/llama3_1/MODEL_CARD.md
@@ -85,7 +85,7 @@ The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a
 
 **License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
 
-Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
+**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
 
 
 ## Intended Use
diff --git a/models/llama3_2/MODEL_CARD.md b/models/llama3_2/MODEL_CARD.md
index 2ff4187..9aea8c6 100644
--- a/models/llama3_2/MODEL_CARD.md
+++ b/models/llama3_2/MODEL_CARD.md
@@ -21,7 +21,7 @@ The Llama 3.2 collection of multilingual large language models (LLMs) is a colle
 
 **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
 
-**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
+**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
 
 ## Intended Use
 
diff --git a/models/llama3_2/MODEL_CARD_VISION.md b/models/llama3_2/MODEL_CARD_VISION.md
index fff9611..abf126a 100644
--- a/models/llama3_2/MODEL_CARD_VISION.md
+++ b/models/llama3_2/MODEL_CARD_VISION.md
@@ -4,7 +4,7 @@ The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a
 
 **Model Developer**: Meta
 
-**Model Architecture:** Llama 3.2-Vision is built on top of Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM.
+**Model Architecture:** Llama 3.2-Vision is built on top of the Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM.
 
 |  | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Data volume | Knowledge cutoff |
 | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
@@ -23,7 +23,7 @@ Developers may fine-tune Llama 3.2 models for languages beyond these supported l
 
 **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
 
-**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2-Vision in applications, please go [here](https://github.com/meta-llama/llama-recipes).
+**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2-Vision in applications, please go [here](https://github.com/meta-llama/llama-recipes).
 
 ## Intended Use
 
@@ -124,7 +124,7 @@ As part of our Responsible release approach, we followed a three-pronged strateg
 
 ### New Capabilities and Use Cases
 
-**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.,
+**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
 
 **Image Reasoning:** Llama 3.2-Vision models come with multimodal (text and image) input capabilities enabling image reasoning applications. As part of our responsible release process, we took dedicated measures including evaluations and mitigations to address the risk of the models uniquely identifying individuals in images. As with other LLM risks, models may not always be robust to adversarial prompts, and developers should evaluate identification and other applicable risks in the context of their applications as well as consider deploying Llama Guard 3-11B-Vision as part of their system or other mitigations as appropriate to detect and mitigate such risks.