metadata

inference: false
license: other

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Camel AI's CAMEL 13B Role Playing Data GPTQ

These files are GPTQ 4bit model files for Camel AI's CAMEL 13B Role Playing Data.

It is the result of quantising to 4bit using AutoGPTQ.

Repositories available

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

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ.
Click Download.
The model will start downloading. Once it's finished it will say "Done"
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: CAMEL-13B-Role-Playing-Data-GPTQ
The model will automatically load, and is now ready for use!
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.

Note that you do not need to set GPTQ parameters any more. These are set automatically from the file quantize_config.json.

Once you're ready, click the Text Generation tab and enter a prompt to get started!

How to use this GPTQ model from Python code

First make sure you have AutoGPTQ installed:

pip install auto-gptq

Then try the following example code:

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse

model_name_or_path = "TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ"
model_basename = "camel-13b-roleplay-GPTQ-4bit-128g.no-act.order"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

Provided files

camel-13b-roleplay-GPTQ-4bit-128g.no-act.order.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

camel-13b-roleplay-GPTQ-4bit-128g.no-act.order.safetensors
- Works with AutoGPTQ in CUDA or Triton modes.
- Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode.
- Works with text-generation-webui, including one-click-installers.
- Parameters: Groupsize = 128. Act Order / desc_act = False.

Discord

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Thanks to the chirper.ai team!

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Camel AI's CAMEL 13B Role Playing Data

CAMEL-13B-Role-Playing-Data is a chat large language model obtained by finetuning LLaMA-13B model on a total of 229K conversations created through our role-playing framework proposed in CAMEL. We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-13B scores an average of 57.2, outperfroming LLaMA-30B (56.9)!

Model	size	ARC-C (25 shots, acc_norm)	HellaSwag (10 shots, acc_norm)	MMLU (5 shots, acc_norm)	TruthfulQA (0 shot, mc2)	Average	Delta
LLaMA	13B	50.8	78.9	37.7	39.9	51.8	-
Vicuna	13B	47.4	75.2	39.6	49.8	53.7	1.9
CAMEL	13B	54.9	79.3	48.5	46.2	57.2	5.4
LLaMA	30B	57.1	82.6	45.7	42.3	56.9	5.1

TheBloke
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CAMEL-13B-Role-Playing-Data-GPTQ