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Add Error Message for not login, Change the readme format, Change the repo name
Browse files
app.py
CHANGED
@@ -11,7 +11,6 @@ from gradio_huggingfacehub_search import HuggingfaceHubSearch
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from apscheduler.schedulers.background import BackgroundScheduler
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from datetime import datetime
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import numpy as np
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import shutil
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HF_TOKEN = os.environ.get("HF_TOKEN")
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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@@ -195,7 +194,11 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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export_to_org, repo_owner, org_token, oauth_token: gr.OAuthToken | None):
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if oauth_token is None or oauth_token.token is None:
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raise gr.Error("You must be logged in to use GGUF-my-repo")
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-
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user_info = whoami(oauth_token.token)
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username = user_info["name"]
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user_orgs = user_info.get("orgs", [])
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@@ -205,7 +208,7 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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logger.info(f"Time {current_time}, Username {username}, Model_ID, {model_id}, q_method {','.join(q_method)}")
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repo_namespace = get_repo_namespace(repo_owner, username, user_orgs)
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model_name = model_id.split('/')[-1]
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try:
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@@ -226,8 +229,7 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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fp16 = str(Path(outdir)/f"{model_name}.fp16.gguf")
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with tempfile.TemporaryDirectory(dir=downloads_dir) as tmpdir:
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print(
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logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Start download")
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local_dir = Path(tmpdir)/model_name
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api.snapshot_download(repo_id=model_id, local_dir=local_dir, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
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@@ -236,16 +238,12 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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if os.path.exists(adapter_config_dir) and not os.path.exists(config_dir):
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raise Exception("adapter_config.json is present. If converting LoRA, use GGUF-my-lora.")
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print(
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logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Download successfully")
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result = subprocess.run(["python", CONVERSION_SCRIPT, local_dir, "--outtype", "f16", "--outfile", fp16], shell=False, capture_output=True)
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print(
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logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Converted to f16")
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if result.returncode != 0:
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raise Exception(f"Error converting to fp16: {result.stderr.decode()}")
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shutil.rmtree(downloads_dir)
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imatrix_path = Path(outdir)/"imatrix.dat"
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if use_imatrix:
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train_data_path = train_data_file.name if train_data_file else "llama.cpp/groups_merged.txt"
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@@ -258,8 +256,7 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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gguf_files = []
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for method in quant_methods:
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print(
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logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Begin quantize")
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name = f"{model_name.lower()}-{method.lower()}-{suffix}.gguf" if suffix else f"{model_name.lower()}-{method.lower()}.gguf"
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path = str(Path(outdir)/name)
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quant_cmd = ["./llama.cpp/llama-quantize", "--imatrix", imatrix_path, fp16, path, method] if use_imatrix else ["./llama.cpp/llama-quantize", fp16, path, method]
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@@ -269,10 +266,9 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
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size = os.path.getsize(path)/1024/1024/1024
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gguf_files.append((name, path, size, method))
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print(
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logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Quantize successfully!")
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suffix_for_repo = f"{imatrix_q_method}-imat" if use_imatrix else "-".join(quant_methods)
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repo_id = f"{repo_namespace}/{model_name}-
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new_repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=private_repo)
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try:
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@@ -420,6 +416,7 @@ with gr.Blocks(css=".gradio-container {overflow-y: auto;}") as demo:
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iface.render()
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def restart_space():
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HfApi().restart_space(repo_id="Antigma/quantize-my-repo", token=HF_TOKEN, factory_reboot=True)
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from apscheduler.schedulers.background import BackgroundScheduler
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from datetime import datetime
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import numpy as np
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HF_TOKEN = os.environ.get("HF_TOKEN")
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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export_to_org, repo_owner, org_token, oauth_token: gr.OAuthToken | None):
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if oauth_token is None or oauth_token.token is None:
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raise gr.Error("You must be logged in to use GGUF-my-repo")
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try:
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whoami(oauth_token.token)
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except Exception as e:
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raise gr.Error("You must be logged in to use GGUF-my-repo")
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user_info = whoami(oauth_token.token)
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username = user_info["name"]
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user_orgs = user_info.get("orgs", [])
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current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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logger.info(f"Time {current_time}, Username {username}, Model_ID, {model_id}, q_method {','.join(q_method)}")
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repo_namespace = get_repo_namespace(repo_owner, username, user_orgs)
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model_name = model_id.split('/')[-1]
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try:
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fp16 = str(Path(outdir)/f"{model_name}.fp16.gguf")
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with tempfile.TemporaryDirectory(dir=downloads_dir) as tmpdir:
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print("Downloading")
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local_dir = Path(tmpdir)/model_name
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api.snapshot_download(repo_id=model_id, local_dir=local_dir, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
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if os.path.exists(adapter_config_dir) and not os.path.exists(config_dir):
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raise Exception("adapter_config.json is present. If converting LoRA, use GGUF-my-lora.")
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print("Download successfully")
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result = subprocess.run(["python", CONVERSION_SCRIPT, local_dir, "--outtype", "f16", "--outfile", fp16], shell=False, capture_output=True)
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print("Converted to f16")
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if result.returncode != 0:
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raise Exception(f"Error converting to fp16: {result.stderr.decode()}")
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imatrix_path = Path(outdir)/"imatrix.dat"
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if use_imatrix:
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train_data_path = train_data_file.name if train_data_file else "llama.cpp/groups_merged.txt"
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gguf_files = []
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for method in quant_methods:
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print("Begin quantize")
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name = f"{model_name.lower()}-{method.lower()}-{suffix}.gguf" if suffix else f"{model_name.lower()}-{method.lower()}.gguf"
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path = str(Path(outdir)/name)
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quant_cmd = ["./llama.cpp/llama-quantize", "--imatrix", imatrix_path, fp16, path, method] if use_imatrix else ["./llama.cpp/llama-quantize", fp16, path, method]
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size = os.path.getsize(path)/1024/1024/1024
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gguf_files.append((name, path, size, method))
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print("Quantize successfully!")
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suffix_for_repo = f"{imatrix_q_method}-imat" if use_imatrix else "-".join(quant_methods)
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repo_id = f"{repo_namespace}/{model_name}-GGUF"
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new_repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=private_repo)
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try:
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iface.render()
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def restart_space():
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HfApi().restart_space(repo_id="Antigma/quantize-my-repo", token=HF_TOKEN, factory_reboot=True)
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