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README.md

@@ -1,12 +1,14 @@
----
-title: TensorLM For HF
-emoji: 📈
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TensorLM - webui for LLM models
+
+![preview](assets/preview.png)
+
+![settings-preview](assets/settings-preview.png)
+
+This is simple and modern [Gradio](https://gradio.app) webui for LLM models GGML format (.bin) based on LLaMA.
+
+## How to install
+
+It is very simple and easy. Go to releases on Github project page and download latest portable version.
+
+Next, open README.md in release and follow the instructions.
+

license.txt

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+
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requirements.txt

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+gradio==4.1.0
+transformers
+torch==2.0.1
+llama-cpp-python==0.1.73
+huggingface_hub
+python-dotenv
+art==6.1

start.bat

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+@echo off
+
+git pull
+
+pip install -q -r requirements.txt
+
+python webui.py

style.css

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+#container{
+    margin: 0 auto;
+    max-width: 40rem;
+}
+#intro{
+    max-width: 100%;
+    text-align: center;
+    margin: 0 auto;
+}
+#generate_button {
+    color: white;
+    border-color: #007bff;
+    background: #007bff;
+    width: 200px;
+    height: 50px;
+}
+footer {
+    visibility: hidden
+}

webui.py

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+
+from art import *
+import time
+
+print(text2art('''TensorLM''', font="small"))
+print("Our license: https://www.apache.org/licenses/LICENSE-2.0.txt")
+
+
+time.sleep(5)
+
+print(" ")
+
+import os
+import gradio as gr
+import copy
+import llama_cpp
+from llama_cpp import Llama
+import random
+from huggingface_hub import hf_hub_download  
+
+#from blip.blip_engine import blip_run
+
+
+
+dir = os.getcwd()
+
+def load_model(path, n_ctx, n_gpu_layers, n_threads, verbose, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, n_batch, last_n_tokens_size, low_vram, rope_freq_base, rope_freq_scale):
+    try:
+        global llm
+        llm = Llama(
+            model_path=f"{dir}\models\{path}",
+            n_ctx=n_ctx,
+            n_gpu_layers=n_gpu_layers,
+            n_threads=n_threads,
+            verbose=verbose,
+            f16_kv=f16_kv,
+            logits_all=logits_all,
+            vocab_only=vocab_only,
+            use_mmap=use_mmap,
+            use_mlock=use_mlock,
+            n_batch=n_batch,
+            last_n_tokens_size=last_n_tokens_size,
+            low_vram=low_vram,
+            rope_freq_base=rope_freq_base,
+            rope_freq_scale=rope_freq_scale,
+
+
+
+        )
+        return path 
+    except:
+        return ""
+
+def list_models(name):
+    return os.listdir(f"{dir}\models")
+
+def render_md(text):
+    return f"{text}"
+
+def download_model(repo_id, filename):
+    hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        local_dir="models",
+        force_download=True, resume_download=False,
+        cache_dir=".cache",
+    )
+    return f"Downloaded!"
+
+history = []
+
+'''
+system_message = """
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+"""
+'''
+
+
+def generate_text(message, history, system_prompt, preset, temperature, max_tokens, top_p, top_k, repeat_penalty):
+    temp = ""
+    input_prompt = f"[INST] <<SYS>>\nYou are {preset}. {system_prompt}.\n<</SYS>>\n\n "
+    for interaction in history:
+        input_prompt = input_prompt + str(interaction[0]) + " [/INST] " + str(interaction[1]) + " </s><s> [INST] "
+
+    input_prompt = input_prompt + str(message) + " [/INST] "
+
+    output = llm(
+        input_prompt,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k, 
+        repeat_penalty=repeat_penalty,
+        max_tokens=max_tokens,
+        stop=[
+            "<|prompter|>",
+            "<|endoftext|>",
+            "<|endoftext|> \n",
+            "ASSISTANT:",
+            "USER:",
+            "SYSTEM:",
+        ],
+        stream=True,
+    )
+    for out in output:
+        stream = copy.deepcopy(out)
+        temp += stream["choices"][0]["text"]
+        yield temp
+
+    history = ["init", input_prompt]
+
+
+    
+chatbot = gr.Chatbot(show_label=False, layout="panel", show_copy_button=True, height=500, min_width=180)
+
+with gr.Blocks(theme="theme-repo/STONE_Theme", title="TensorLM", css="style.css") as demo:
+    with gr.Row():
+        model = gr.Dropdown(label="Model (only based on Llama in GGML format (.bin))", choices=os.listdir(f"{dir}\models"), value="None", interactive=True, allow_custom_value=False, scale=50)
+        #refresh_model = gr.Button(value="Load model", interactive=True, scale=1)
+    with gr.Row():
+        with gr.Tab("💬"):
+            with gr.Row(visible=False, render=False) as sliders:
+        
+                with gr.Tab("Parameters"):
+                    max_tokens = gr.Slider(label="Max new tokens", minimum=256, maximum=4056, value=512, step=8, interactive=True)
+                    temperature = gr.Slider(label="Temperature", minimum=0.01, maximum=2.00, value=0.15, step=0.01, interactive=True)
+                    top_p = gr.Slider(label="Top P", minimum=0.01, maximum=2.00, value=0.10, step=0.01, interactive=True)
+                    top_k = gr.Slider(label="Top K", minimum=10.00, maximum=100.00, value=40.00, step=0.01, interactive=True)
+                    repeat_penalty = gr.Slider(label="Repeat penalty", minimum=0.01, maximum=2.00, value=1.10, step=0.01, interactive=True)
+                with gr.Tab("Instructions"):
+                    preset = gr.Dropdown(label="Prompt preset", choices=["AI-assistant", "Historical Expert", "Math Tutor", "Python Tutor", "Language Learning Coach", "Philosopher", "Poet"], value="AI-assistant", interactive=True, allow_custom_value=False)
+                    system_prompt = gr.Textbox(label="Custom system prompt", max_lines=4, lines=3, interactive=True)
+
+            with gr.Row():
+                gr.ChatInterface(
+                    generate_text,
+                    chatbot=chatbot,
+                    retry_btn="🔄️",
+                    submit_btn="📨",
+                    undo_btn="↩️",
+                    clear_btn="🗑️",
+                    additional_inputs=[system_prompt, preset, temperature, max_tokens, top_k, top_k, repeat_penalty]
+                )
+                
+
+            sliders_change = gr.Checkbox(label="Options", value=False, interactive=True)
+            with gr.Row():
+                sliders.render()
+            
+
+        with gr.Tab("💽"):
+            gr.Markdown("## Download model from 🤗 HuggingFace.co")
+            with gr.Row():
+                repo_id = gr.Textbox(label="REPO_ID",  value="ehristoforu/LLMs", lines=1, max_lines=1, interactive=False)
+                filename = gr.Dropdown(label="FILENAME", interactive=True, choices=["llama-2-7b-chat.ggmlv3.q2_K.bin", "llama-2-13b-chat.ggmlv3.q2_K.bin", "codellama-7b-instruct.ggmlv3.Q2_K.bin", "codellama-13b-instruct.ggmlv3.Q2_K.bin", "saiga-13b.ggmlv3.Q4_1.bin", "saiga-30b.ggmlv3.Q3_K.bin"], value="", allow_custom_value=False)
+                download_btn = gr.Button(value="Download")
+                logs=gr.Markdown()
+        with gr.Tab("📒"):
+            with gr.Tab("Notebook"):
+                with gr.Row():
+                    notebook = gr.Textbox(show_label=False, value="This is a great day...", placeholder="Your notebook", max_lines=40, lines=35, interactive=True)
+            with gr.Tab("Markdown"):
+                render_markdown = gr.Button(value="Render markdown from Notebook", interactive=True)
+                with gr.Row():
+                    markdown = gr.Markdown()
+
+        with gr.Tab("⚙️"):
+            with gr.Row():
+                with gr.Column():
+                    #with gr.Row():
+                    #    gr.Markdown("### Style")
+                    #    chat_style = gr.Dropdown(label="Style of chat", choices=["bubble", "panel"], value="bubble", interactive=True, allow_custom_value=False)
+                    with gr.Row():
+                        gr.Markdown("### Engine")
+                        reload_model = gr.Button("Apply settings to model", interactive=True)
+                        n_ctx = gr.Slider(label="Number of CTX", minimum=1024, maximum=4056, value=2048, step=8, interactive=True)
+                        n_gpu_layers = gr.Slider(label="Number of GPU layers", minimum=0, maximum=36, value=0, step=1, interactive=True)
+                        n_threads = gr.Slider(label="Number of Threads", minimum=2, maximum=36, value=4, step=1, interactive=True)
+                        verbose = gr.Checkbox(label="Verbose", value=True, interactive=True)
+                        f16_kv = gr.Checkbox(label="F16 KV", value=True, interactive=True)
+                        logits_all = gr.Checkbox(label="Logits all", value=False, interactive=True)
+                        vocab_only = gr.Checkbox(label="Vocab only", value=False, interactive=True)
+                        use_mmap = gr.Checkbox(label="Use mmap", value=True, interactive=True)
+                        use_mlock = gr.Checkbox(label="Use mlock", value=False, interactive=True)
+                        n_batch = gr.Slider(label="Number of batch", minimum=128, maximum=2048, value=512, step=8, interactive=True)
+                        last_n_tokens_size = gr.Slider(label="Last number of tokens size", minimum=8, maximum=512, value=64, step=8, interactive=True)
+                        low_vram = gr.Checkbox(label="Low VRAM", value=False, interactive=True)
+                        rope_freq_base = gr.Slider(label="Rope freq base", minimum=1000.0, maximum=30000.0, value=10000.0, step=0.1, interactive=True)
+                        rope_freq_scale = gr.Slider(label="Rope freq scale", minimum=0.1, maximum=3.0, value=1.0, step=0.1)
+
+    with gr.Row():
+        gr.Markdown("""
+        <center><a href="https://gradio.app">gradio 4.1.0</a> | <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> | <a href="https://python.org">python</a> | <a href="https://huggingface.co/TheBloke?search_models=GGML">Suggested models</a></center>
+        """)    
+    
+    render_markdown.click(
+        fn=render_md,
+        inputs=notebook,
+        outputs=markdown,
+        queue=False,
+        api_name=False,
+    )
+
+    sliders_change.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=sliders_change,
+        outputs=sliders,
+        queue=False,
+        api_name=False,
+    )
+
+    
+
+
+    download_btn.click(download_model, inputs=[repo_id, filename], outputs=logs, api_name=False, queue=False)
+                
+    model.change(load_model, inputs=[model, n_ctx, n_gpu_layers, n_threads, verbose, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, n_batch, last_n_tokens_size, low_vram, rope_freq_base, rope_freq_scale], outputs=model, api_name=False, queue=False)
+    reload_model.click(load_model, inputs=[model, n_ctx, n_gpu_layers, n_threads, verbose, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, n_batch, last_n_tokens_size, low_vram, rope_freq_base, rope_freq_scale], outputs=model, api_name=False, queue=False)
+
+
+
+demo.launch(
+    inbrowser=True,
+    server_port=5555,
+    debug=False,
+    quiet=True,
+    favicon_path="assets/favicon.png",
+)