Hugging Face's logo

llmware
/
slim-q-gen-tiny

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Model card Files Community
slim-q-gen-tiny
File size: 3,023 Bytes 
aacc98d
 
 
 
 
bae318e
aacc98d
 
 
bae318e
aacc98d
 
 
bae318e
aacc98d
bae318e
 
 
aacc98d
 
 
 
 
 
 
 
 
 
 
 
 
bae318e
 
aacc98d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bae318e
aacc98d
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
 
---
license: apache-2.0
inference: false
---

# SLIM-Q-GEN-TINY  

<!-- Provide a quick summary of what the model is/does. -->

**slim-q-gen-tiny** implements a specialized function-calling question generation from a context passage, with output in the form of a python dictionary, e.g.,  

&nbsp;&nbsp;&nbsp;&nbsp;`{'question': ['What were earnings per share in the most recent quarter?'] }  

This model is finetuned on top of a tinyllama-1.1b base, and is intended for fast, local prototyping.  

For fast inference use, we would recommend the 'quantized tool' version, e.g.,  [**slim-q-gen-tiny-tool**](https://huggingface.co/llmware/slim-q-gen-tiny-tool).  

You also may want to checkout the finetuned phi-3 version of this model, e.g.,  [**slim-q-gen-phi-3-tool**](https://huggingface.co/llmware/slim-q-gen-phi-3-tool).  


## Prompt format:

`function = "generate"`  
`params = "{'question', 'boolean', or 'multiple choice'}"`  
`prompt = "<human> " + {text} + "\n" + `  
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  


<details>
<summary>Transformers Script </summary>

    model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-tiny")
    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-gen-tiny")

    function = "generate"
    params = "boolean"

    text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue."  
    
    prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"

    inputs = tokenizer(prompt, return_tensors="pt")
    start_of_input = len(inputs.input_ids[0])

    outputs = model.generate(
        inputs.input_ids.to('cpu'),
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        temperature=0.7,
        max_new_tokens=200
    )

    output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)

    print("output only: ", output_only)  

    [OUTPUT]:  {'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} }  
    
    # here's the fun part
    try:
        output_only = ast.literal_eval(llm_string_output)
        print("success - converted to python dictionary automatically")
    except:
        print("fail - could not convert to python dictionary automatically - ", llm_string_output)
   
   </details>  
 
<details>  



    
<summary>Using as Function Call in LLMWare</summary>

    from llmware.models import ModelCatalog
    slim_model = ModelCatalog().load_model("llmware/slim-q-gen-tiny", sample=True, temperature=0.7)  
    response = slim_model.function_call(text,params=["boolean"], function="generate")  

    print("llmware - llm_response: ", response)  

</details>  

    
## Model Card Contact

Darren Oberst & llmware team  

[Join us on Discord](https://discord.gg/MhZn5Nc39h)