Add VB's app

.gitattributes

@@ -19,6 +19,7 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pickle filter=lfs diff=lfs merge=lfs -text
 *.pkl filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,7 +1,139 @@
+import os
+import shutil
+import subprocess
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!"
+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",
+            placeholder="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            value="TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+        ),
+        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!",
+    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>",
+    
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch(server_name="0.0.0.0")
+# Launch the interface
+iface.launch(server_name="0.0.0.0", debug=True)