Text Generation
English
Flexan commited on
Commit
a2e7342
·
verified ·
1 Parent(s): 914b5fd

Add README

Browse files
Files changed (1) hide show
  1. README.md +105 -3
README.md CHANGED
@@ -1,3 +1,105 @@
1
- ---
2
- license: cc-by-sa-4.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-sa-4.0
3
+ datasets:
4
+ - PJMixers-Dev/dolphin-deepseek-1k-think-1k-response-filtered-ShareGPT
5
+ - Jofthomas/hermes-function-calling-thinking-V1
6
+ language:
7
+ - en
8
+ base_model:
9
+ - arlineka/CatNyanster-7b
10
+ pipeline_tag: text-generation
11
+ ---
12
+ # Model Card for Blake-XTM Arc
13
+
14
+ Blake-XTM Arc is a 7B large language model used for text generation.
15
+ It was trained to reason and optionally call provided tools.
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ Blake-XTM Arc is a 7B parameter instruct LLM trained to think and optionally call a tool. It only supports using one tool per assistant message (no parallel tool calling).
22
+ The model was LoRA fine-tuned with [CatNyanster-7B](https://huggingface.co/arlineka/CatNyanster-7b) as base model, which was fine-tuned on [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
23
+
24
+ ### Chat Format
25
+
26
+ Blake-XTM Arc uses the ChatML format, e.g.:
27
+ ```text
28
+ <|im_start|>system
29
+ System message<|im_end|>
30
+ <|im_start|>user
31
+ User prompt<|im_end|>
32
+ <|im_start|>assistant
33
+ Assistant response<|im_end|>
34
+ ```
35
+
36
+ ### Model Usage
37
+
38
+ The assistant response can have the following three formats (the contents are examples and were not generated from the model):
39
+ 1. Only response:
40
+ ```text
41
+ <|im_start|>assistant
42
+ Hello! How may I assist you today?<|im_end|>
43
+ ```
44
+ 2. Thought process and response:
45
+ ```text
46
+ <|im_start|>assistant
47
+ <|think_start|>The user has greeted me with a simple message. I should think about how to respond to them.
48
+
49
+ Since the user sent a simple greeting, I should reply with a greeting that matches their energy.
50
+
51
+ Alright, I can reply with a message like 'Hello! How can I help you?'<|think_end|>
52
+
53
+ Hello! How may I assist you today?<|im_end|>
54
+ ```
55
+ 3. Thought process and tool call:
56
+ ```text
57
+ <|im_start|>assistant
58
+ <|think_start|>The user has asked me to find all restaurants near Paris. Hmm... let me think this through thoroughly.
59
+
60
+ I can see that I have a tool available called 'find_restaurants', which I might be able to use for this purpose.
61
+
62
+ Alright, I think I should use the `find_restaurants` tool to find the restaurants near Paris. For the `city` parameter, I'll use 'Paris', and for the `country` parameter, I'll fill in `France`.
63
+
64
+ Okay, I can go ahead and make the tool call now.<|think_end|>
65
+
66
+ <|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|>
67
+ ```
68
+
69
+ **Warning:** The model seems to bias towards thought process + response, even for short prompts like "Hello," which may cause it to overthink.
70
+
71
+ We recommend using the following system prompts for your situation:
72
+ - Only thought process:
73
+ ```text
74
+ You are an advanced reasoning model.
75
+
76
+ You think between <|think_start|>...<|think_end|> tags. You must think if the user's request involves math or logical thinking/reasoning.
77
+ ```
78
+ - Thought process and tool calling:
79
+ ```text
80
+ You are an advanced reasoning model with tool-calling capabilities.
81
+
82
+ You think between <|think_start|>...<|think_end|> tags. You must think if the user's request involves math, logical thinking/reasoning, or when you want to consider using a tool.
83
+
84
+ # Tools
85
+ You have access to the following tools:
86
+ [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert currency from one type to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to be converted'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'get_random_joke', 'description': 'Get a random joke', 'parameters': {'type': 'object', 'properties': {}, 'required': []}}}] <\/tools>Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
87
+
88
+ To call a tool, write a JSON object with the name and arguments inside <|tool_start|>...<|tool_end|>.
89
+ ```
90
+
91
+ For responding with a tool response, you can send a message as the `tool` user:
92
+ ```
93
+ <|im_start|>assistant
94
+ <|think_start|>The user has asked me to find all restaurants near Paris. Hmm... let me think this through thoroughly.
95
+
96
+ I can see that I have a tool available called 'find_restaurants', which I might be able to use for this purpose.
97
+
98
+ Alright, I think I should use the `find_restaurants` tool to find the restaurants near Paris. For the `city` parameter, I'll use 'Paris', and for the `country` parameter, I'll fill in `France`.
99
+
100
+ Okay, I can go ahead and make the tool call now.<|think_end|>
101
+
102
+ <|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|>
103
+ <|im_start|>tool
104
+ {'restaurants': [{'name': 'A Restaurant Name', 'rating': 4.5}]}<|im_end|>
105
+ ```