Add generated vicuna 13b modle files

README.md

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+
+# vicuna-13b
+
+This README provides a step-by-step guide to set up and run the FastChat application with the required dependencies and model.
+
+## Prerequisites
+
+Before you proceed, ensure that you have `git` installed on your system.
+
+## Installation
+
+Follow the steps below to install the required packages and set up the environment.
+
+1. Upgrade `pip`:
+
+```bash
+python3 -m pip install --upgrade pip
+```
+
+2. Install `accelerate`:
+
+```bash
+python3 -m pip install accelerate
+```
+
+3. Install `bitsandbytes`
+
+3.1 install by pip
+
+```bash
+python3 -m pip install bitsandbytes
+```
+
+3.2 Clone the `bitsandbytes` repository and install it:
+
+```bash
+git clone https://github.com/TimDettmers/bitsandbytes.git
+cd bitsandbytes
+CUDA_VERSION=118 make cuda11x
+python3 -m pip install .
+cd ..
+```
+
+use the following command to find `CUDA_VERSION`:
+```bash
+ls /usr/local/cuda*
+```
+
+4. Clone the `FastChat` repository and install it:
+
+```bash
+git clone https://github.com/lm-sys/FastChat.git
+cd FastChat
+python3 -m pip install -e .
+cd ..
+```
+
+5. Install `git-lfs`:
+
+```bash
+curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
+sudo apt-get install git-lfs
+git lfs install
+```
+
+6. Clone the `vicuna-13b` model:
+
+```bash
+git clone https://huggingface.co/helloollel/vicuna-13b
+```
+
+## Running FastChat
+
+After completing the installation, you can run FastChat with the following command:
+
+```bash
+python3 -m fastchat.serve.cli --model-name ./vicuna-13b
+```
+
+This will start FastChat using the `vicuna-13b` model.
+
+## Running in Notebook
+
+```python
+import argparse
+import time
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
+
+from fastchat.conversation import conv_templates, SeparatorStyle
+from fastchat.serve.monkey_patch_non_inplace import replace_llama_attn_with_non_inplace_operations
+
+
+def load_model(model_name, device, num_gpus, load_8bit=False):
+    if device == "cpu":
+        kwargs = {}
+    elif device == "cuda":
+        kwargs = {"torch_dtype": torch.float16}
+        if load_8bit:
+            if num_gpus != "auto" and int(num_gpus) != 1:
+                print("8-bit weights are not supported on multiple GPUs. Revert to use one GPU.")
+            kwargs.update({"load_in_8bit": True, "device_map": "auto"})
+        else:
+            if num_gpus == "auto":
+                kwargs["device_map"] = "auto"
+            else:
+                num_gpus = int(num_gpus)
+                if num_gpus != 1:
+                    kwargs.update({
+                        "device_map": "auto",
+                        "max_memory": {i: "13GiB" for i in range(num_gpus)},
+                    })
+    elif device == "mps":
+        # Avoid bugs in mps backend by not using in-place operations.
+        kwargs = {"torch_dtype": torch.float16}
+        replace_llama_attn_with_non_inplace_operations()
+    else:
+        raise ValueError(f"Invalid device: {device}")
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+    model = AutoModelForCausalLM.from_pretrained(model_name,
+        low_cpu_mem_usage=True, **kwargs)
+
+    # calling model.cuda() mess up weights if loading 8-bit weights
+    if device == "cuda" and num_gpus == 1 and not load_8bit:
+        model.to("cuda")
+    elif device == "mps":
+        model.to("mps")
+
+    return model, tokenizer
+
+
+@torch.inference_mode()
+def generate_stream(tokenizer, model, params, device,
+                    context_len=2048, stream_interval=2):
+    """Adapted from fastchat/serve/model_worker.py::generate_stream"""
+
+    prompt = params["prompt"]
+    l_prompt = len(prompt)
+    temperature = float(params.get("temperature", 1.0))
+    max_new_tokens = int(params.get("max_new_tokens", 256))
+    stop_str = params.get("stop", None)
+
+    input_ids = tokenizer(prompt).input_ids
+    output_ids = list(input_ids)
+
+    max_src_len = context_len - max_new_tokens - 8
+    input_ids = input_ids[-max_src_len:]
+
+    for i in range(max_new_tokens):
+        if i == 0:
+            out = model(
+                torch.as_tensor([input_ids], device=device), use_cache=True)
+            logits = out.logits
+            past_key_values = out.past_key_values
+        else:
+            attention_mask = torch.ones(
+                1, past_key_values[0][0].shape[-2] + 1, device=device)
+            out = model(input_ids=torch.as_tensor([[token]], device=device),
+                        use_cache=True,
+                        attention_mask=attention_mask,
+                        past_key_values=past_key_values)
+            logits = out.logits
+            past_key_values = out.past_key_values
+
+        last_token_logits = logits[0][-1]
+
+        if device == "mps":
+            # Switch to CPU by avoiding some bugs in mps backend.
+            last_token_logits = last_token_logits.float().to("cpu")
+
+        if temperature < 1e-4:
+            token = int(torch.argmax(last_token_logits))
+        else:
+            probs = torch.softmax(last_token_logits / temperature, dim=-1)
+            token = int(torch.multinomial(probs, num_samples=1))
+
+        output_ids.append(token)
+
+        if token == tokenizer.eos_token_id:
+            stopped = True
+        else:
+            stopped = False
+
+        if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
+            output = tokenizer.decode(output_ids, skip_special_tokens=True)
+            pos = output.rfind(stop_str, l_prompt)
+            if pos != -1:
+                output = output[:pos]
+                stopped = True
+            yield output
+
+        if stopped:
+            break
+
+    del past_key_values
+
+args = dict(
+    model_name='./vicuna-13b',
+    device='cuda',
+    num_gpus='1',
+    load_8bit=True,
+    conv_template='v1',
+    temperature=0.7,
+    max_new_tokens=512,
+    debug=False
+)
+
+args = argparse.Namespace(**args)
+
+model_name = args.model_name
+
+# Model
+model, tokenizer = load_model(args.model_name, args.device,
+    args.num_gpus, args.load_8bit)
+
+# Chat
+conv = conv_templates[args.conv_template].copy()
+
+def chat(inp):
+  conv.append_message(conv.roles[0], inp)
+  conv.append_message(conv.roles[1], None)
+  prompt = conv.get_prompt()
+
+  params = {
+      "model": model_name,
+      "prompt": prompt,
+      "temperature": args.temperature,
+      "max_new_tokens": args.max_new_tokens,
+      "stop": conv.sep if conv.sep_style == SeparatorStyle.SINGLE else conv.sep2,
+  }
+
+  print(f"{conv.roles[1]}: ", end="", flush=True)
+  pre = 0
+  for outputs in generate_stream(tokenizer, model, params, args.device):
+      outputs = outputs[len(prompt) + 1:].strip()
+      outputs = outputs.split(" ")
+      now = len(outputs)
+      if now - 1 > pre:
+          print(" ".join(outputs[pre:now-1]), end=" ", flush=True)
+          pre = now - 1
+  print(" ".join(outputs[pre:]), flush=True)
+
+  conv.messages[-1][-1] = " ".join(outputs)
+```
+
+```python
+chat("what's the meaning of life?")
+```

config.json

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+{
+  "_name_or_path": "/content/drive/MyDrive/AI/fastchat/13B_hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32001
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.0.dev0"
+}

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