Update app.py

app.py

@@ -28,11 +28,51 @@ def script_to_use(model_id, api):
     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, private_repo, oauth_token: gr.OAuthToken | None):
+def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
+    if oauth_token.token is None:
+        raise ValueError("You have to be logged in.")
+    
+    split_cmd = f"llama.cpp/gguf-split --split --split-max-tensors {split_max_tensors}"
+    if split_max_size:
+        split_cmd += f" --split-max-size {split_max_size}"
+    split_cmd += f" {model_path} {model_path.split('.')[0]}"
+    
+    print(f"Split command: {split_cmd}") 
+    
+    result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True)
+    print(f"Split command stdout: {result.stdout}") 
+    print(f"Split command stderr: {result.stderr}") 
+    
+    if result.returncode != 0:
+        raise Exception(f"Error splitting the model: {result.stderr}")
+    print("Model split successfully!")
+     
+    
+    sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])]
+    if sharded_model_files:
+        print(f"Sharded model files: {sharded_model_files}")
+        api = HfApi(token=oauth_token.token)
+        for file in sharded_model_files:
+            file_path = os.path.join('.', file)
+            print(f"Uploading file: {file_path}")
+            try:
+                api.upload_file(
+                    path_or_fileobj=file_path,
+                    path_in_repo=file,
+                    repo_id=repo_id,
+                )
+            except Exception as e:
+                raise Exception(f"Error uploading file {file_path}: {e}")
+    else:
+        raise Exception("No sharded files found.")
+    
+    print("Sharded model has been uploaded successfully!")
+
+def process_model(model_id, q_method, private_repo, split_model, split_max_tensors, split_max_size, oauth_token: gr.OAuthToken | None):
     if oauth_token.token is None:
         raise ValueError("You must be logged in to use GGUF-my-repo")
     model_name = model_id.split('/')[-1]
-    fp16 = f"{model_name}/{model_name.lower()}.fp16.bin"
+    fp16 = f"{model_name}.fp16.gguf"
 
     try:
         api = HfApi(token=oauth_token.token)
@@ -54,7 +94,9 @@ def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken |
         dl_pattern += pattern
 
         api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
-        print("Model downloaded successully!")
+        print("Model downloaded successfully!")
+        print(f"Current working directory: {os.getcwd()}")
+        print(f"Model directory contents: {os.listdir(model_name)}")
 
         conversion_script = script_to_use(model_id, api)
         fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
@@ -62,17 +104,21 @@ def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken |
         print(result)
         if result.returncode != 0:
             raise Exception(f"Error converting to fp16: {result.stderr}")
-        print("Model converted to fp16 successully!")
+        print("Model converted to fp16 successfully!")
+        print(f"Converted model path: {fp16}")
 
-        qtype = f"{model_name}/{model_name.lower()}.{q_method.upper()}.gguf"
-        quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}"
+        username = whoami(oauth_token.token)["name"]
+        quantized_gguf_name = f"{model_name.lower()}-{q_method.lower()}.gguf"
+        quantized_gguf_path = quantized_gguf_name
+        quantise_ggml = f"./llama.cpp/quantize {fp16} {quantized_gguf_path} {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!")
+        print(f"Quantized successfully with {q_method} option!")
+        print(f"Quantized model path: {quantized_gguf_path}")
 
         # Create empty repo
-        new_repo_url = api.create_repo(repo_id=f"{model_name}-{q_method}-GGUF", exist_ok=True, private=private_repo)
+        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{q_method}-GGUF", exist_ok=True, private=private_repo)
         new_repo_id = new_repo_url.repo_id
         print("Repo created successfully!", new_repo_url)
 
@@ -90,50 +136,49 @@ def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken |
             This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
             Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
             ## Use with llama.cpp
-
             Install llama.cpp through brew.
-
             ```bash
             brew install ggerganov/ggerganov/llama.cpp
             ```
             Invoke the llama.cpp server or the CLI.
-
             CLI:
-
             ```bash
-            llama-cli --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is"
+            llama-cli --hf-repo {new_repo_id} --model {quantized_gguf_name} -p "The meaning to life and the universe is"
             ```
-
             Server:
-
             ```bash
-            llama-server --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -c 2048
+            llama-server --hf-repo {new_repo_id} --model {quantized_gguf_name} -c 2048
             ```
-
             Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
-
             ```
-            git clone https://github.com/ggerganov/llama.cpp && \
-            cd llama.cpp && \
-            make && \
-            ./main -m {qtype.split("/")[-1]} -n 128
+            git clone https://github.com/ggerganov/llama.cpp && \\
+            cd llama.cpp && \\
+            make && \\
+            ./main -m {quantized_gguf_name} -n 128
             ```
             """
         )
-        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,
-        )
+        card.save(f"README.md")
+
+        if split_model:
+            split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
+        else:
+            try:
+                print(f"Uploading quantized model: {quantized_gguf_path}")
+                api.upload_file(
+                    path_or_fileobj=quantized_gguf_path,
+                    path_in_repo=quantized_gguf_name,
+                    repo_id=new_repo_id,
+                )
+            except Exception as e:
+                raise Exception(f"Error uploading quantized model: {e}")
 
         api.upload_file(
-            path_or_fileobj=f"{model_name}/README-new.md",
-            path_in_repo="README.md",
+            path_or_fileobj=f"README.md",
+            path_in_repo=f"README.md",
             repo_id=new_repo_id,
         )
-        print("Uploaded successfully!")
+        print(f"Uploaded successfully with {q_method} option!")
 
         return (
             f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
@@ -147,38 +192,75 @@ def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken |
 
 
 # Create Gradio interface
-iface = gr.Interface(
-    fn=process_model,
-    inputs=[
-        HuggingfaceHubSearch(
-            label="Hub Model ID",
-            placeholder="Search for model id on Huggingface",
-            search_type="model",
-        ),
-        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.Checkbox(
-            value=False,
-            label="Private Repo",
-            info="Create a private repo under your username."
-        ),
-    ],
-    outputs=[
-        gr.Markdown(label="output"),
-        gr.Image(show_label=False),
-    ],
-    title="Create your own GGUF Quants, blazingly fast ⚡!",
-    description="The space takes an HF repo as an input, quantises it and creates a Public repo containing the selected quant under your HF user namespace.",
-)
 with gr.Blocks() as demo:
     gr.Markdown("You must be logged in to use GGUF-my-repo.")
     gr.LoginButton(min_width=250)
-    iface.render()
+
+    model_id_input = HuggingfaceHubSearch(
+        label="Hub Model ID",
+        placeholder="Search for model id on Huggingface",
+        search_type="model",
+    )
+
+    q_method_input = 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 quantization type",
+        value="Q4_K_M",
+        filterable=False
+    )
+
+    private_repo_input = gr.Checkbox(
+        value=False,
+        label="Private Repo",
+        info="Create a private repo under your username."
+    )
+
+    split_model_input = gr.Checkbox(
+        value=False,
+        label="Split Model",
+        info="Shard the model using gguf-split."
+    )
+
+    split_max_tensors_input = gr.Number(
+        value=256,
+        label="Max Tensors per File",
+        info="Maximum number of tensors per file when splitting model.",
+        visible=False
+    )
+
+    split_max_size_input = gr.Textbox(
+        label="Max File Size",
+        info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.",
+        visible=False
+    )
+
+    iface = gr.Interface(
+        fn=process_model,
+        inputs=[
+            model_id_input,
+            q_method_input,
+            private_repo_input,
+            split_model_input,
+            split_max_tensors_input,
+            split_max_size_input,
+        ],
+        outputs=[
+            gr.Markdown(label="output"),
+            gr.Image(show_label=False),
+        ],
+        title="Create your own GGUF Quants, blazingly fast ⚡!",
+        description="The space takes an HF repo as an input, quantizes it and creates a Public repo containing the selected quant under your HF user namespace.",
+    )
+
+    def update_visibility(split_model):
+        return gr.update(visible=split_model), gr.update(visible=split_model)
+
+    split_model_input.change(
+        fn=update_visibility,
+        inputs=split_model_input,
+        outputs=[split_max_tensors_input, split_max_size_input]
+    )
 
 def restart_space():
     HfApi().restart_space(repo_id="ggml-org/gguf-my-repo", token=HF_TOKEN, factory_reboot=True)