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cinematika-7B-v0.1-AWQ

Text Generation
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mistral
text-generation-inference
4-bit precision
awq
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TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Cinematika 7B v0.1 - AWQ

Description

This repo contains AWQ model files for Jon Durbin's Cinematika 7B v0.1.

These files were quantised using hardware kindly provided by Massed Compute.

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

It is supported by:

Repositories available

Prompt template: Amazon 

<|prompter|>{prompt}</s><|assistant|>

Provided files, and AWQ parameters

I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

Models are released as sharded safetensors files.

Branch Bits GS AWQ Dataset Seq Len Size
main 4 128 VMware Open Instruct 4096 4.15 GB

How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui.

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

  1. Click the Model tab.
  2. Under Download custom model or LoRA, enter TheBloke/cinematika-7B-v0.1-AWQ.
  3. Click Download.
  4. The model will start downloading. Once it's finished it will say "Done".
  5. In the top left, click the refresh icon next to Model.
  6. In the Model dropdown, choose the model you just downloaded: cinematika-7B-v0.1-AWQ
  7. Select Loader: AutoAWQ.
  8. Click Load, and the model will load and is now ready for use.
  9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
  10. Once you're ready, click the Text Generation tab and enter a prompt to get started!

Multi-user inference server: vLLM

Documentation on installing and using vLLM can be found here.

  • Please ensure you are using vLLM version 0.2 or later.
  • When using vLLM as a server, pass the --quantization awq parameter.

For example:

python3 -m vllm.entrypoints.api_server --model TheBloke/cinematika-7B-v0.1-AWQ --quantization awq --dtype auto
  • When using vLLM from Python code, again set quantization=awq.

For example:

from vllm import LLM, SamplingParams

prompts = [
    "Tell me about AI",
    "Write a story about llamas",
    "What is 291 - 150?",
    "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|prompter|>{prompt}</s><|assistant|>
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/cinematika-7B-v0.1-AWQ", quantization="awq", dtype="auto")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Multi-user inference server: Hugging Face Text Generation Inference (TGI)

Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

--model-id TheBloke/cinematika-7B-v0.1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

pip3 install huggingface-hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''<|prompter|>{prompt}</s><|assistant|>
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: ", response)

Inference from Python code using Transformers

Install the necessary packages 

pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"

Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.

If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:

pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .

Transformers example code (requires Transformers 4.35.0 and later) 

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "TheBloke/cinematika-7B-v0.1-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "Tell me about AI"
prompt_template=f'''<|prompter|>{prompt}</s><|assistant|>
'''

# Convert prompt to tokens
tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

generation_params = {
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 512,
    "repetition_penalty": 1.1
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

Compatibility

The files provided are tested to work with:

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Aemon Algiz.

Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Jon Durbin's Cinematika 7B v0.1

Cinematika

Cinematika

cinematika-7b-v0.1 is a fine-tune of MistralLite on the cinematika-v0.1 dataset

The dataset is comprised of 211 movie scripts converted to novel style, multi-character RP data.

Prompt format

For RP, there is no prompt format, really, it's just plain text with name prefix.

If you wish to use this model to parse new scripts, create character cards, or other types of instructions, you will want to use the same prompt format as the mistrallite base model, e.g.:

<|prompter|>Create a character card for a panda named Po.  Po is a giant panda who was improbably chosen as the "Dragon Warrior", the kung fu champion of the Valley of Peace.</s><|assistant|>

Example character card 

name: Rorschach
characteristics:
  Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission.
  Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone.
  Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills.
  Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature.
  Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime.
  Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive.
  Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals.
  Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing.
  Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats.
  Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding.

description: |
  Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger.
  Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated.
  He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed.
  Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos.

example_dialogue: |
  Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key."
  {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?"
  Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent."
  {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger."
  Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this."
  {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?"
  Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn."

Example, with guided scenario 

[characters]
name: Rorschach
... (remainder of character card)

[scenario]
Hollis Mason reflects on his past as the original Nite Owl, reminiscing about the early days of masked heroes and the formation of the Watchmen.
He discusses the absurdity of the superhero world and the encounters he had with various villains.
Dan Dreiberg, the second Nite Owl, joins the conversation and they share a moment of camaraderie before Dan leaves.
The news of Rorschach's actions serves as a reminder of the legacy of masked heroes that still persists.
[/scenario]

Usage

Essentially, you want to use pure text completion with stop tokens for "{your name}: "

The format the model was trained on is as follows:

[characters]
{character card 1}
{character card 2}
{your character card, even just name: Jon}

NPCS:
- Shopkeeper
- Bank teller
[/characters]

[scenario]
Brief description of the scenario/setting for the chat.
[/scenario]

{first character you'd like to speak}:

For example, to use with vllm, you would first run:

python -m vllm.entrypoints.openai.api_server --model ./cinematika-7b-v0.1 --host 127.0.0.1 --port 8801 --served-model-name cinematika-7b-v0.1

Here's a really crude python script example to show how you could interact with it:

import requests
import json

prompt = """name: Rorschach
characteristics:
  Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission.
  Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone.
  Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills.
  Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature.
  Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime.
  Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive.
  Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals.
  Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing.
  Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats.
  Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding.
description: |
  Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger.
  Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated.
  He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed.
  Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos.
example_dialogue: |
  Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key."
  {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?"
  Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent."
  {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger."
  Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this."
  {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?"
  Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn."

name: Jon
description:
  Rorschach's arch nemesis, the original Chupacabra.

[scenario]
Jon and Rorschach find themselves in a cave, dimly lit only by a small fire started by a lightning strike nearby.  The storm rages on, and the duo prepare to find to the death.
[/scenario]

Rorschach: """

while True:
    response = requests.post("http://127.0.0.1:8801/v1/completions", json={
        "prompt": prompt,
        "max_tokens": 1024,
        "temperature": 0.3,
        "stop": ["\nJon: ", "Jon: "],
    }).json()["choices"][0]["text"].strip()
    response = re.sub('("[^"]+")', r'\033[96m\1\033[00m', response)
    print(f"\033[92mRorschach:\033[00m {response}")
    prompt += response.rstrip() + "\n\nJon: "
    next_line = input("Jon: ")
    prompt += "Jon: " + next_line.strip() + "\n\nRorschach: "

Mac example

On mac, you can get started easily with LMStudio and SillyTavern.

LMStudio:

Load the model and set all the prompt values to "", or just import this preset (adjust threads and antiprompt):

{
  "name": "Exported from LM Studio on 12/1/2023, 4:19:30 AM",
  "load_params": {
    "n_ctx": 32000,
    "n_batch": 512,
    "rope_freq_base": 10000,
    "rope_freq_scale": 1,
    "n_gpu_layers": 1,
    "use_mlock": true,
    "main_gpu": 0,
    "tensor_split": [
      0
    ],
    "seed": -1,
    "f16_kv": true,
    "use_mmap": true
  },
  "inference_params": {
    "n_threads": 14,
    "n_predict": -1,
    "top_k": 40,
    "top_p": 0.95,
    "temp": 0.8,
    "repeat_penalty": 1.1,
    "input_prefix": "",
    "input_suffix": "",
    "antiprompt": [
      "Jon:",
      "Jon: "
    ],
    "pre_prompt": "",
    "pre_prompt_suffix": "",
    "pre_prompt_prefix": "",
    "seed": -1,
    "tfs_z": 1,
    "typical_p": 1,
    "repeat_last_n": 64,
    "frequency_penalty": 0,
    "presence_penalty": 0,
    "n_keep": 0,
    "logit_bias": {},
    "mirostat": 0,
    "mirostat_tau": 5,
    "mirostat_eta": 0.1,
    "memory_f16": true,
    "multiline_input": false,
    "penalize_nl": true
  }
}

Then, start the server, and be sure "Automatic Propmt Formatting" is off.

Within SillyTavern:

  • Set API to Text Completion, API type to Aphrodite, and API URL to http://127.0.0.1:8801 (adjust port to the value you use in LMStudio)
  • Set Context template to Default, disable instruct mode, use preset Roleplay, and enable "Always add character's name to prompt"

There are probably better presets - this is just something I tested quickly.

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