Update app.py

app.py

@@ -17,81 +17,15 @@ from apscheduler.schedulers.background import BackgroundScheduler
 from textwrap import dedent
 
 HF_TOKEN = os.environ.get("HF_TOKEN")
+OLLAMA_PUB = os.environ.get("OLLAMA_PUB")
+OLLAMA_USERNAME = os.environ.get("OLLAMA_USERNAME")
 
-def generate_importance_matrix(model_path, train_data_path):
-    imatrix_command = f"./llama-imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10"
 
-    os.chdir("llama.cpp")
-
-    print(f"Current working directory: {os.getcwd()}")
-    print(f"Files in the current directory: {os.listdir('.')}")
-
-    if not os.path.isfile(f"../{model_path}"):
-        raise Exception(f"Model file not found: {model_path}")
-
-    print("Running imatrix command...")
-    process = subprocess.Popen(imatrix_command, shell=True)
-
-    try:
-        process.wait(timeout=60)  # added wait
-    except subprocess.TimeoutExpired:
-        print("Imatrix computation timed out. Sending SIGINT to allow graceful termination...")
-        process.send_signal(signal.SIGINT)
-        try:
-            process.wait(timeout=5)  # grace period
-        except subprocess.TimeoutExpired:
-            print("Imatrix proc still didn't term. Forecfully terming process...")
-            process.kill()
-
-    os.chdir("..")
-
-    print("Importance matrix generation completed.")
-
-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/llama-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, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token: gr.OAuthToken | None):
+def process_model(model_id, q_method, 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}.fp16.gguf"
+    ollama = f"{model_name}.fp16.gguf"
 
     try:
         api = HfApi(token=oauth_token.token)
@@ -117,156 +51,26 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
         print(f"Current working directory: {os.getcwd()}")
         print(f"Model directory contents: {os.listdir(model_name)}")
 
-        conversion_script = "convert-hf-to-gguf.py"
-        fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
-        result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
+        model_file = {model_name}_ollama
+        f = open("{model_file}", "w")
+        print(f.write("From {model_id}")) 
+        ollama_conversion = f"ollama create -f {model_file} {OLLAMA_USERNAME}/{model_id}:{q_method}"
+        ollama_push =  f"ollama push {OLLAMA_USERNAME}/{model_id}:{q_method}"
+        
+        result = subprocess.run(ollama_conversion, shell=True, capture_output=True)
         print(result)
         if result.returncode != 0:
-            raise Exception(f"Error converting to fp16: {result.stderr}")
-        print("Model converted to fp16 successfully!")
-        print(f"Converted model path: {fp16}")
-
-        imatrix_path = "llama.cpp/imatrix.dat"
-
-        if use_imatrix:
-            if train_data_file:
-                train_data_path = train_data_file.name
-            else:
-                train_data_path = "groups_merged.txt" #fallback calibration dataset
-
-            print(f"Training data file path: {train_data_path}")
-
-            if not os.path.isfile(train_data_path):
-                raise Exception(f"Training data file not found: {train_data_path}")
-
-            generate_importance_matrix(fp16, train_data_path)
-        else:
-            print("Not using imatrix quantization.")
-        username = whoami(oauth_token.token)["name"]
-        quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf"
-        quantized_gguf_path = quantized_gguf_name
-        if use_imatrix:
-            quantise_ggml = f"./llama.cpp/llama-quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
-        else:
-            quantise_ggml = f"./llama.cpp/llama-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(f"Quantized successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-        print(f"Quantized model path: {quantized_gguf_path}")
-
-        # Create empty repo
-        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF", exist_ok=True, private=private_repo)
-        new_repo_id = new_repo_url.repo_id
-        print("Repo created successfully!", new_repo_url)
-
-        try:
-            card = ModelCard.load(model_id, token=oauth_token.token)
-        except:
-            card = ModelCard("")
-        if card.data.tags is None:
-            card.data.tags = []
-        card.data.tags.append("llama-cpp")
-        card.data.tags.append("gguf-my-repo")
-        card.data.base_model = model_id
-        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 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 (works on Mac and Linux)
-            
-            ```bash
-            brew install llama.cpp
-            
-            ```
-            Invoke the llama.cpp server or the CLI.
-            
-            ### CLI:
-            ```bash
-            llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
-            ```
-            
-            ### Server:
-            ```bash
-            llama-server --hf-repo {new_repo_id} --hf-file {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.
-
-            Step 1: Clone llama.cpp from GitHub.
-            ```
-            git clone https://github.com/ggerganov/llama.cpp
-            ```
-
-            Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-            ```
-            cd llama.cpp && LLAMA_CURL=1 make
-            ```
-
-            Step 3: Run inference through the main binary.
-            ```
-            ./llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
-            ```
-            or 
-            ```
-            ./llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
-            ```
-            """
-        )
-        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}")
-        
-        
-        imatrix_path = "llama.cpp/imatrix.dat"
-        if os.path.isfile(imatrix_path):
-            try:
-                print(f"Uploading imatrix.dat: {imatrix_path}")
-                api.upload_file(
-                    path_or_fileobj=imatrix_path,
-                    path_in_repo="imatrix.dat",
-                    repo_id=new_repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading imatrix.dat: {e}")
-
-        api.upload_file(
-            path_or_fileobj=f"README.md",
-            path_in_repo=f"README.md",
-            repo_id=new_repo_id,
-        )
-        print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-
-        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!")
+            raise Exception(f"Error converting to Ollama: {result.stderr}")
+        print("Model converted to Ollama successfully!")
+        print(f"Converted model path: {ollama}")
+       
 
 css="""/* Custom CSS to allow scrolling */
 .gradio-container {overflow-y: auto;}
 """
 # Create Gradio interface
 with gr.Blocks(css=css) as demo: 
-    gr.Markdown("You must be logged in to use GGUF-my-repo.")
+    gr.Markdown("You must be logged in to use Ollamafy.")
     gr.LoginButton(min_width=250)
 
     model_id = HuggingfaceHubSearch(
@@ -275,103 +79,33 @@ with gr.Blocks(css=css) as demo:
         search_type="model",
     )
 
-    q_method = 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"],
+     = gr.Dropdown(
+        ## ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
+        ["Q3_k_s", "Q3_k_m", "Q3_k_l", "Q4_0", "Q4_1", "Q4_k_s", "Q4_k_m", "Q5_0", "Q5_1", "Q5_k_s", "Q5_k_m", "Q6_k","Q8_0"],
         label="Quantization Method",
-        info="GGML quantization type",
-        value="Q4_K_M",
+        info="Ollama Quantization Types",
+        value="ALL", 
         filterable=False,
         visible=True
     )
 
-    imatrix_q_method = gr.Dropdown(
-        ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
-        label="Imatrix Quantization Method",
-        info="GGML imatrix quants type",
-        value="IQ4_NL", 
-        filterable=False,
-        visible=False
-    )
-
-    use_imatrix = gr.Checkbox(
-        value=False,
-        label="Use Imatrix Quantization",
-        info="Use importance matrix for quantization."
-    )
-
-    private_repo = gr.Checkbox(
-        value=False,
-        label="Private Repo",
-        info="Create a private repo under your username."
-    )
-
-    train_data_file = gr.File(
-        label="Training Data File",
-        file_types=["txt"],
-        visible=False
-    )
-
-    split_model = gr.Checkbox(
-        value=False,
-        label="Split Model",
-        info="Shard the model using gguf-split."
-    )
-
-    split_max_tensors = gr.Number(
-        value=256,
-        label="Max Tensors per File",
-        info="Maximum number of tensors per file when splitting model.",
-        visible=False
-    )
-
-    split_max_size = 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
-    )
-
-    def update_visibility(use_imatrix):
-        return gr.update(visible=not use_imatrix), gr.update(visible=use_imatrix), gr.update(visible=use_imatrix)
-    
-    use_imatrix.change(
-        fn=update_visibility,
-        inputs=use_imatrix,
-        outputs=[q_method, imatrix_q_method, train_data_file]
-    )
-
     iface = gr.Interface(
         fn=process_model,
         inputs=[
             model_id,
             q_method,
-            use_imatrix,
-            imatrix_q_method,
-            private_repo,
-            train_data_file,
-            split_model,
-            split_max_tensors,
-            split_max_size,
         ],
         outputs=[
             gr.Markdown(label="output"),
             gr.Image(show_label=False),
         ],
-        title="Create your own GGUF Quants, blazingly fast ⚡!",
+        title="Create your own Ollama Models and Push them to the Ollama Library, 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.",
         api_name=False
     )
 
-    def update_split_visibility(split_model):
-        return gr.update(visible=split_model), gr.update(visible=split_model)
-
-    split_model.change(
-        fn=update_split_visibility,
-        inputs=split_model,
-        outputs=[split_max_tensors, split_max_size]
-    )
-
 def restart_space():
-    HfApi().restart_space(repo_id="ggml-org/gguf-my-repo", token=HF_TOKEN, factory_reboot=True)
+    HfApi().restart_space(repo_id="unclemusclez/ollamafy", token=HF_TOKEN, factory_reboot=True)
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=21600)