File size: 4,829 Bytes
08e5ef1
7edda8b
2bede7c
 
7edda8b
 
2bede7c
 
75b770e
08e5ef1
 
 
2bede7c
7686e09
 
 
 
 
 
 
 
 
 
 
2bede7c
75b770e
5696fee
 
7686e09
9781999
 
75b770e
5696fee
9781999
7686e09
9781999
5696fee
9781999
 
 
 
 
5696fee
9781999
 
 
 
 
 
 
5696fee
 
 
9781999
 
 
 
 
5696fee
9781999
 
 
 
 
 
 
 
 
5696fee
9781999
 
 
5696fee
9781999
 
 
5696fee
9781999
 
 
 
5696fee
9781999
 
 
5696fee
9781999
5696fee
9781999
 
 
 
5696fee
9781999
 
 
 
 
5696fee
9781999
 
2bede7c
 
 
 
 
7edda8b
 
 
7686e09
7edda8b
 
 
 
7686e09
 
b416bb7
7edda8b
 
 
 
7686e09
 
7edda8b
2bede7c
7cd57ad
 
 
 
87f5ccd
7cd57ad
 
 
2bede7c
 
 
 
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
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
import os
import shutil
import subprocess

import gradio as gr

from huggingface_hub import create_repo, HfApi
from huggingface_hub import snapshot_download
from huggingface_hub import whoami
from huggingface_hub import ModelCard

from textwrap import dedent

LLAMA_LIKE_ARCHS = ["MistralForCausalLM", "LlamaForCausalLM"]

def script_to_use(model_id, api):
    info = api.model_info(model_id)
    if info.config is None:
        return None
    arch = info.config.get("architectures", None)
    if arch is None:
        return None
    arch = arch[0]
    return "convert.py" if arch in LLAMA_LIKE_ARCHS else "convert-hf-to-gguf.py"

def process_model(model_id, q_method, hf_token):
    model_name = model_id.split('/')[-1]
    fp16 = f"{model_name}/{model_name.lower()}.fp16.bin"
    
    try:
        api = HfApi(token=hf_token)

        snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False)
        print("Model downloaded successully!")
        
        conversion_script = script_to_use(model_id, api)
        fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
        result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
        if result.returncode != 0:
            raise Exception(f"Error converting to fp16: {result.stderr}")
        print("Model converted to fp16 successully!")

        qtype = f"{model_name}/{model_name.lower()}.{q_method.upper()}.gguf"
        quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}"
        result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
        if result.returncode != 0:
            raise Exception(f"Error quantizing: {result.stderr}")
        print("Quantised successfully!")

        # Create empty repo
        new_repo_url = api.create_repo(repo_id=f"{model_name}-{q_method}-GGUF", exist_ok=True)
        new_repo_id = new_repo_url.repo_id
        print("Repo created successfully!", new_repo_url)

        card = ModelCard.load(model_id)
        card.data.tags = ["llama-cpp"] if card.data.tags is None else card.data.tags + ["llama-cpp"]
        card.text = dedent(
            f"""
            # {new_repo_id}
            This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp.
            Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
            ## Use with llama.cpp

            ```bash
            brew install ggerganov/ggerganov/llama.cpp
            ```

            ```bash
            llama-cli --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is "
            ```

            ```bash
            llama-server --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -c 2048
            ```
            """
        )
        card.save(os.path.join(model_name, "README-new.md"))

        api.upload_file(
            path_or_fileobj=qtype,
            path_in_repo=qtype.split("/")[-1],
            repo_id=new_repo_id,
        )

        api.upload_file(
            path_or_fileobj=f"{model_name}/README-new.md",
            path_in_repo="README.md",
            repo_id=new_repo_id,
        )
        print("Uploaded successfully!")

        return (
            f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
            "llama.png",
        )
    except Exception as e:
        return (f"Error: {e}", "error.png")
    finally:
        shutil.rmtree(model_name, ignore_errors=True)
        print("Folder cleaned up successfully!")


# Create Gradio interface
iface = gr.Interface(
    fn=process_model, 
    inputs=[
        gr.Textbox(
            lines=1, 
            label="Hub Model ID",
            info="Model repo ID",
        ),
        gr.Dropdown(
            ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"], 
            label="Quantization Method", 
            info="GGML quantisation type",
            value="Q4_K_M",
            filterable=False
        ),
        gr.Textbox(
            lines=1, 
            label="HF Write Token",
            info="https://hf.co/settings/token",
            type="password",
        )
    ], 
    outputs=[
        gr.Markdown(label="output"),
        gr.Image(show_label=False),
    ],
    title="Create your own GGUF Quants, blazingly fast ⚡!",
    description="Create GGUF quants from any Hugging Face repository! You need to specify a write token obtained in https://hf.co/settings/tokens.",
    article="<p>Find your write token at <a href='https://huggingface.co/settings/tokens' target='_blank'>token settings</a></p>",
    
)

# Launch the interface
iface.launch(debug=True)