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README.md

@@ -1,139 +1,139 @@
----
-language:
-- en
-pipeline_tag: text-generation
-tags:
-- fireplace
-- fireplace-2
-- valiant
-- valiant-labs
-- llama
-- llama-3.1
-- llama-3.1-instruct
-- llama-3.1-instruct-8b
-- llama-3
-- llama-3-instruct
-- llama-3-instruct-8b
-- 8b
-- function-calling
-- sql
-- database
-- data-visualization
-- matplotlib
-- json
-- conversational
-- chat
-- instruct
-model_type: llama
-license: llama3.1
----
-
-
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qg49GOlx8zogDOrMTnb89.jpeg)
-
-
-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](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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.](https://huggingface.co/blog/llama31#custom-tool-calling)
-
-
-## 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](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
-- [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
-- [sequelbox/Cadmium](https://huggingface.co/datasets/sequelbox/Cadmium)
-- [sequelbox/Harlequin](https://huggingface.co/datasets/sequelbox/Harlequin)
-- [migtissera/Tess-v1.5](https://huggingface.co/datasets/migtissera/Tess-v1.5)
-- [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove)
-
-Additional capabilities will be added to future releases.
-
-
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)
-
-
-Fireplace 2 is created by [Valiant Labs.](http://valiantlabs.ca/)
-
-[Check out our HuggingFace page for Shining Valiant 2 and our other models!](https://huggingface.co/ValiantLabs)
-
-[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
-
-We care about open source.
-For everyone to use.
-
+---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- fireplace
+- fireplace-2
+- valiant
+- valiant-labs
+- llama
+- llama-3.1
+- llama-3.1-instruct
+- llama-3.1-instruct-8b
+- llama-3
+- llama-3-instruct
+- llama-3-instruct-8b
+- 8b
+- function-calling
+- sql
+- database
+- data-visualization
+- matplotlib
+- json
+- conversational
+- chat
+- instruct
+model_type: llama
+license: llama3.1
+---
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/JYkaXrk2DqpXhaL9WymKY.jpeg)
+
+
+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](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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.](https://huggingface.co/blog/llama31#custom-tool-calling)
+
+
+## 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](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
+- [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
+- [sequelbox/Cadmium](https://huggingface.co/datasets/sequelbox/Cadmium)
+- [sequelbox/Harlequin](https://huggingface.co/datasets/sequelbox/Harlequin)
+- [migtissera/Tess-v1.5](https://huggingface.co/datasets/migtissera/Tess-v1.5)
+- [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove)
+
+Additional capabilities will be added to future releases.
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)
+
+
+Fireplace 2 is created by [Valiant Labs.](http://valiantlabs.ca/)
+
+[Check out our HuggingFace page for Shining Valiant 2 and our other models!](https://huggingface.co/ValiantLabs)
+
+[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
+
+We care about open source.
+For everyone to use.
+
 We encourage others to finetune further from our models.