Update README.md

README.md

@@ -130,6 +130,80 @@ model-index:
 This model was converted to GGUF format from [`ValiantLabs/Llama3.1-8B-Fireplace2`](https://huggingface.co/ValiantLabs/Llama3.1-8B-Fireplace2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/ValiantLabs/Llama3.1-8B-Fireplace2) for more details on the model.
 
+---
+Model details:
+-
+Fireplace 2 is a chat model, adding helpful structured outputs to Llama 3.1 8b Instruct.
+
+    an expansion pack of supplementary outputs - request them at will within your chat:
+        Inline function calls
+        SQL queries
+        JSON objects
+        Data visualization with matplotlib
+    Mix normal chat and structured outputs within the same conversation.
+    Fireplace 2 supplements the existing strengths of Llama 3.1, providing inline capabilities within the Llama 3 Instruct format.
+
+Version
+
+This is the 2024-07-23 release of Fireplace 2 for Llama 3.1 8b.
+
+We're excited to bring further upgrades and releases to Fireplace 2 in the future.
+
+Help us and recommend Fireplace 2 to your friends!
+Prompting Guide
+
+Fireplace uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat with Llama 3.1 and also includes the different special tokens used for Fireplace 2's added features:
+
+import transformers import torch
+
+model_id = "ValiantLabs/Llama3.1-8B-Fireplace2"
+
+pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", )
+
+messages = [ {"role": "system", "content": "You are Fireplace, an expert technical assistant."}, {"role": "user", "content": "Hi, can you explain local area networking to me?"}, #general Llama 3.1 chat #{"role": "user", "content": "I have the following SQL table: employees (job_id VARCHAR, salary INTEGER)\n\nCan you find all employees with a salary above $75000?<|request_sql|>"}, #for SQL query #{"role": "user", "content": "{""name"": ""get_news_headlines"",""description"": ""Get the latest news headlines"",""parameters"": {""type"": ""object"",""properties"": {""country"": {""type"": ""string"",""description"": ""The country for which news headlines are to be retrieved""}},""required"": [""country""]}}\n\nHi, can you get me the latest news headlines for the United States?<|request_function_call|>"}, # for function call #{"role": "user", "content": "Show me an example of a histogram with a fixed bin size. Use attractive colors.<|request_matplotlib|>"}, #for data visualization #{"role": "user", "content": "Can you define the word 'presence' for me, thanks!<|request_json|>"}, #for JSON output ]
+
+outputs = pipeline( messages, max_new_tokens=512, ) print(outputs[0]["generated_text"][-1])
+
+While Fireplace 2 is trained to minimize incorrect structured outputs, they can still occur occasionally. Production uses of Fireplace 2 should verify the structure of all model outputs and remove any unneeded components of the output.
+
+For handling of function call responses, use the Llama 3.1 Instruct tool response style.
+Special Tokens
+
+Fireplace 2 utilizes special tokens applied to the Llama 3.1 tokenizer:
+
+    <|request_json|>
+    <|start_json|>
+    <|end_json|>
+    <|request_sql|>
+    <|start_sql|>
+    <|end_sql|>
+    <|request_matplotlib|>
+    <|start_matplotlib|>
+    <|end_matplotlib|>
+    <|request_function_call|>
+    <|start_function_call|>
+    <|end_function_call|>
+
+These are supplemental to the existing special tokens used by Llama 3.1, such as <|python_tag|> and <|start_header_id|>. Fireplace 2 has been trained using the Llama 3.1 Instruct chat structure, with new special tokens added within the conversation.
+
+The 'request' tokens are used by the user to request a specific type of structured output. They should be appended to the end of the user's message and can be alternated with normal chat responses throughout the conversation.
+The Model
+
+Fireplace 2 is built on top of Llama 3.1 8b Instruct.
+
+This version of Fireplace 2 uses data from the following datasets:
+
+    glaiveai/glaive-function-calling-v2
+    b-mc2/sql-create-context
+    sequelbox/Cadmium
+    sequelbox/Harlequin
+    migtissera/Tess-v1.5
+    LDJnr/Pure-Dove
+
+Additional capabilities will be added to future releases.
+
+---
+
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)