reach-vb HF staff Wauplin HF staff commited on
Commit
5696fee
1 Parent(s): 9781999

Some cleaning in huggingface_hub integration (#13)

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- Some cleaning in huggingface_hub integration (85fe931f3911f59e26cc6c4ca2f28c5d1affa26a)


Co-authored-by: Lucain Pouget <[email protected]>

Files changed (1) hide show
  1. app.py +17 -26
app.py CHANGED
@@ -24,25 +24,23 @@ def script_to_use(model_id, api):
24
  return "convert.py" if arch in LLAMA_LIKE_ARCHS else "convert-hf-to-gguf.py"
25
 
26
  def process_model(model_id, q_method, hf_token):
27
- MODEL_NAME = model_id.split('/')[-1]
28
- fp16 = f"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.bin"
29
 
30
  try:
31
  api = HfApi(token=hf_token)
32
 
33
- username = whoami(hf_token)["name"]
34
-
35
- snapshot_download(repo_id=model_id, local_dir = f"{MODEL_NAME}", local_dir_use_symlinks=False)
36
  print("Model downloaded successully!")
37
 
38
  conversion_script = script_to_use(model_id, api)
39
- fp16_conversion = f"python llama.cpp/{conversion_script} {MODEL_NAME} --outtype f16 --outfile {fp16}"
40
  result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
41
  if result.returncode != 0:
42
  raise Exception(f"Error converting to fp16: {result.stderr}")
43
  print("Model converted to fp16 successully!")
44
 
45
- qtype = f"{MODEL_NAME}/{MODEL_NAME.lower()}.{q_method.upper()}.gguf"
46
  quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}"
47
  result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
48
  if result.returncode != 0:
@@ -50,20 +48,15 @@ def process_model(model_id, q_method, hf_token):
50
  print("Quantised successfully!")
51
 
52
  # Create empty repo
53
- repo_id = f"{username}/{MODEL_NAME}-{q_method}-GGUF"
54
- repo_url = create_repo(
55
- repo_id = repo_id,
56
- repo_type="model",
57
- exist_ok=True,
58
- token=hf_token
59
- )
60
- print("Repo created successfully!")
61
 
62
  card = ModelCard.load(model_id)
63
  card.data.tags = ["llama-cpp"] if card.data.tags is None else card.data.tags + ["llama-cpp"]
64
  card.text = dedent(
65
  f"""
66
- # {repo_id}
67
  This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp.
68
  Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
69
  ## Use with llama.cpp
@@ -73,39 +66,37 @@ def process_model(model_id, q_method, hf_token):
73
  ```
74
 
75
  ```bash
76
- llama-cli --hf-repo {repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is "
77
  ```
78
 
79
  ```bash
80
- llama-server --hf-repo {repo_id} --model {qtype.split("/")[-1]} -c 2048
81
  ```
82
  """
83
  )
84
- card.save(os.path.join(MODEL_NAME, "README-new.md"))
85
 
86
  api.upload_file(
87
  path_or_fileobj=qtype,
88
  path_in_repo=qtype.split("/")[-1],
89
- repo_id=repo_id,
90
- repo_type="model",
91
  )
92
 
93
  api.upload_file(
94
- path_or_fileobj=f"{MODEL_NAME}/README-new.md",
95
  path_in_repo="README.md",
96
- repo_id=repo_id,
97
- repo_type="model",
98
  )
99
  print("Uploaded successfully!")
100
 
101
  return (
102
- f'Find your repo <a href=\'{repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
103
  "llama.png",
104
  )
105
  except Exception as e:
106
  return (f"Error: {e}", "error.png")
107
  finally:
108
- shutil.rmtree(MODEL_NAME, ignore_errors=True)
109
  print("Folder cleaned up successfully!")
110
 
111
 
 
24
  return "convert.py" if arch in LLAMA_LIKE_ARCHS else "convert-hf-to-gguf.py"
25
 
26
  def process_model(model_id, q_method, hf_token):
27
+ model_name = model_id.split('/')[-1]
28
+ fp16 = f"{model_name}/{model_name.lower()}.fp16.bin"
29
 
30
  try:
31
  api = HfApi(token=hf_token)
32
 
33
+ snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False)
 
 
34
  print("Model downloaded successully!")
35
 
36
  conversion_script = script_to_use(model_id, api)
37
+ fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
38
  result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
39
  if result.returncode != 0:
40
  raise Exception(f"Error converting to fp16: {result.stderr}")
41
  print("Model converted to fp16 successully!")
42
 
43
+ qtype = f"{model_name}/{model_name.lower()}.{q_method.upper()}.gguf"
44
  quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}"
45
  result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
46
  if result.returncode != 0:
 
48
  print("Quantised successfully!")
49
 
50
  # Create empty repo
51
+ new_repo_url = api.create_repo(repo_id=f"{model_name}-{q_method}-GGUF", exist_ok=True)
52
+ new_repo_id = new_repo_url.repo_id
53
+ print("Repo created successfully!", new_repo_url)
 
 
 
 
 
54
 
55
  card = ModelCard.load(model_id)
56
  card.data.tags = ["llama-cpp"] if card.data.tags is None else card.data.tags + ["llama-cpp"]
57
  card.text = dedent(
58
  f"""
59
+ # {new_repo_id}
60
  This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp.
61
  Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
62
  ## Use with llama.cpp
 
66
  ```
67
 
68
  ```bash
69
+ llama-cli --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is "
70
  ```
71
 
72
  ```bash
73
+ llama-server --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -c 2048
74
  ```
75
  """
76
  )
77
+ card.save(os.path.join(model_name, "README-new.md"))
78
 
79
  api.upload_file(
80
  path_or_fileobj=qtype,
81
  path_in_repo=qtype.split("/")[-1],
82
+ repo_id=new_repo_id,
 
83
  )
84
 
85
  api.upload_file(
86
+ path_or_fileobj=f"{model_name}/README-new.md",
87
  path_in_repo="README.md",
88
+ repo_id=new_repo_id,
 
89
  )
90
  print("Uploaded successfully!")
91
 
92
  return (
93
+ f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
94
  "llama.png",
95
  )
96
  except Exception as e:
97
  return (f"Error: {e}", "error.png")
98
  finally:
99
+ shutil.rmtree(model_name, ignore_errors=True)
100
  print("Folder cleaned up successfully!")
101
 
102