Add pipeline tag and library name

This PR improves the model card by:
* Adding a relevant pipeline tag, ensuring people can find the model at https://huggingface.co/models?pipeline_tag=text-generation
* Adding the `library_name`, enabling the "how to use" widget at the top.

README.md

@@ -1,15 +1,15 @@
 ---
-license: cc-by-nc-4.0
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
 language:
 - en
+license: cc-by-nc-4.0
 metrics:
 - accuracy
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
-
-
 # CoALM-8B: Conversational Agentic Language Model
 
 [![Made with Oumi](https://badgen.net/badge/Made%20with/Oumi/%23085CFF?icon=https%3A%2F%2Foumi.ai%2Flogo_dark.svg)](https://github.com/oumi-ai/oumi)
@@ -51,31 +51,31 @@ CoALM-8B is trained on a **multi-task dataset** covering dialogue state tracking
 ## Capabilities and Features
 
 ### 🗣 Conversational Agentic Abilities
-- **Multi-turn Dialogue Mastery:** Maintains coherent conversations across multiple turns with accurate state tracking.
-- **Function Calling and API Integration:** Dynamically selects and calls APIs for task execution.
-- **ReAct-based Reasoning:** Utilizes a structured reasoning process (User-Thought-Action-Observation-Thought-Response).
+- **Multi-turn Dialogue Mastery:** Maintains coherent conversations across multiple turns with accurate state tracking.\
+- **Function Calling and API Integration:** Dynamically selects and calls APIs for task execution.\
+- **ReAct-based Reasoning:** Utilizes a structured reasoning process (User-Thought-Action-Observation-Thought-Response).\
 - **Zero-Shot Generalization:** Excels in previously unseen function-calling tasks.
 
 ### 🚀 Benchmark Performance
-- **MultiWOZ 2.4 (TOD):** Excels in dialogue state tracking and task completion.
-- **BFCL V3 (LA):** Demonstrates superior function-calling abilities over language agents.
-- **API-Bank (LA):** Accurately generates API calls and integrates responses into conversation flow.
+- **MultiWOZ 2.4 (TOD):** Excels in dialogue state tracking and task completion.\
+- **BFCL V3 (LA):** Demonstrates superior function-calling abilities over language agents.\
+- **API-Bank (LA):** Accurately generates API calls and integrates responses into conversation flow.\
 
 ---
 ## Training Process
 ### 🔧 Fine-tuning Stages
-1. **TOD Fine-tuning:** Optimized for dialogue state tracking (e.g., augmented SNIPS reformatted in Alpaca-style instruction tuning).
-2. **Function Calling Fine-tuning:** Trained to select and generate well-formed API calls from LA datasets.
-3. **ReAct-based Fine-tuning:** Addresses multi-turn conversations with API integration using a structured reasoning framework.
+1. **TOD Fine-tuning:** Optimized for dialogue state tracking (e.g., augmented SNIPS reformatted in Alpaca-style instruction tuning).\
+2. **Function Calling Fine-tuning:** Trained to select and generate well-formed API calls from LA datasets.\
+3. **ReAct-based Fine-tuning:** Addresses multi-turn conversations with API integration using a structured reasoning framework.\
 
 ### 🔍 Training Hyperparameters
-- **Base Model:** Llama 3.1 8B Instruct
-- **LoRA Config:** Rank = 16, Scaling Factor = 32
-- **Batch Size:** 8
-- **Learning Rate:** 1e-4
-- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8)
-- **Precision:** Mixed precision (bfloat16)
-- **Warm-up Steps:** 0.1 ratio of total steps
+- **Base Model:** Llama 3.1 8B Instruct\
+- **LoRA Config:** Rank = 16, Scaling Factor = 32\
+- **Batch Size:** 8\
+- **Learning Rate:** 1e-4\
+- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8)\
+- **Precision:** Mixed precision (bfloat16)\
+- **Warm-up Steps:** 0.1 ratio of total steps\
 - **Gradient Accumulation Steps:** 1
 
 ---
@@ -116,8 +116,8 @@ oumi train -c ./oumi_train.yaml
 ```
 
 ---
-- **Task-Specific Calibration:** While CoALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning.
-- **Scalability to Larger Models:** Future iterations (CoALM-70B, CoALM-405B) extend capabilities to larger-scale agentic conversations.
+- **Task-Specific Calibration:** While CoALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning.\
+- **Scalability to Larger Models:** Future iterations (CoALM-70B, CoALM-405B) extend capabilities to larger-scale agentic conversations.\
 - **Open-Source Expansion:** All datasets, training scripts, and model checkpoints are publicly available to foster further research.
 
 ## Acknowledgements
@@ -142,5 +142,4 @@ If you use **CoALM-8B** in your research, please cite:
 }
 ```
 
-For more details, visit [Project Repository](https://github.com/oumi-ai/oumi/tree/main/configs/projects/calm) or contact **[email protected]**.
-
+For more details, visit [Project Repository](https://github.com/oumi-ai/oumi/tree/main/configs/projects/calm) or contact **[email protected]**.