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This model is a function calling version of microsoft/phi-3.5-mini-instruct finetuned on the Salesforce/xlam-function-calling-60k dataset.
Uploaded model
- Developed by: akshayballal
- License: apache-2.0
- Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit
Usage
from unsloth import FastLanguageModel
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "outputs/checkpoint-3000", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = 1024,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
tools = [
{
"name": "upcoming",
"description": "Fetches upcoming CS:GO matches data from the specified API endpoint.",
"parameters": {
"content_type": {
"description": "The content type for the request, default is 'application/json'.",
"type": "str",
"default": "application/json",
},
"page": {
"description": "The page number to retrieve, default is 1.",
"type": "int",
"default": "1",
},
"limit": {
"description": "The number of matches to retrieve per page, default is 10.",
"type": "int",
"default": "10",
},
},
}
]
messages = [
{
"role": "user",
"content": f"You are a helpful assistant. Below are the tools that you have access to. \n\n### Tools: \n{tools} \n\n### Query: \n{query} \n",
},
]
input = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)
output = model.generate(
input_ids=input, max_new_tokens=512, temperature=0.0
)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
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