Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
System: 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.
User: {prompt}
Assistant:
Provided files
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Branch
Bits
Group Size
Act Order (desc_act)
File Size
ExLlama Compatible?
Made With
Description
main
4
128
False
3.90 GB
True
AutoGPTQ
Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options.
gptq-4bit-32g-actorder_True
4
32
True
4.28 GB
True
AutoGPTQ
4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed.
gptq-4bit-64g-actorder_True
4
64
True
4.02 GB
True
AutoGPTQ
4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-4bit-128g-actorder_True
4
128
True
3.90 GB
True
AutoGPTQ
4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
How to download from branches
In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-32g-actorder_True
1from transformers import AutoTokenizer, pipeline, logging
2from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
34model_name_or_path ="TheBloke/Llama-2-7b-Chat-GPTQ"5model_basename ="gptq_model-4bit-128g"67use_triton =False89tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)1011model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,12 model_basename=model_basename
13 use_safetensors=True,14 trust_remote_code=True,15 device="cuda:0",16 use_triton=use_triton,17 quantize_config=None)1819"""
20To download from a specific branch, use the revision parameter, as in this example:
2122model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
23 revision="gptq-4bit-32g-actorder_True",
24 model_basename=model_basename,
25 use_safetensors=True,
26 trust_remote_code=True,
27 device="cuda:0",
28 quantize_config=None)
29"""3031prompt ="Tell me about AI"32prompt_template=f'''System: 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.
33User: {prompt}34Assistant:
35'''3637print("\n\n*** Generate:")3839input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()40output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)41print(tokenizer.decode(output[0]))4243# Inference can also be done using transformers' pipeline4445# Prevent printing spurious transformers error when using pipeline with AutoGPTQ46logging.set_verbosity(logging.CRITICAL)4748print("*** Pipeline:")49pipe = pipeline(50"text-generation",51 model=model,52 tokenizer=tokenizer,53 max_new_tokens=512,54 temperature=0.7,55 top_p=0.95,56 repetition_penalty=1.1557)5859print(pipe(prompt_template)[0]['generated_text'])
Compatibility
The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
Original model card: Meta's Llama 2 7b Chat
Llama 2
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
Model Details
Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the website and accept our License before requesting access here.
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
Model Developers Meta
Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
Input Models input text only.
Output Models generate text only.
Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
Training Data
Params
Content Length
GQA
Tokens
LR
Llama 2
A new mix of publicly available online data
7B
4k
✗
2.0T
3.0 x 10-4
Llama 2
A new mix of publicly available online data
13B
4k
✗
2.0T
3.0 x 10-4
Llama 2
A new mix of publicly available online data
70B
4k
✔
2.0T
1.5 x 10-4
Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
Model Dates Llama 2 was trained between January 2023 and July 2023.
Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and breaklines in between (we recommend calling strip() on inputs to avoid double-spaces). See our reference code in github for details: chat_completion.
Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
Hardware and Software
Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Time (GPU hours)
Power Consumption (W)
Carbon Emitted(tCO2eq)
Llama 2 7B
184320
400
31.22
Llama 2 13B
368640
400
62.44
Llama 2 70B
1720320
400
291.42
Total
3311616
539.00
CO2 emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
Training Data
Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
Model
Size
Code
Commonsense Reasoning
World Knowledge
Reading Comprehension
Math
MMLU
BBH
AGI Eval
Llama 1
7B
14.1
60.8
46.2
58.5
6.95
35.1
30.3
23.9
Llama 1
13B
18.9
66.1
52.6
62.3
10.9
46.9
37.0
33.9
Llama 1
33B
26.0
70.0
58.4
67.6
21.4
57.8
39.8
41.7
Llama 1
65B
30.7
70.7
60.5
68.6
30.8
63.4
43.5
47.6
Llama 2
7B
16.8
63.9
48.9
61.3
14.6
45.3
32.6
29.3
Llama 2
13B
24.5
66.9
55.4
65.8
28.7
54.8
39.4
39.1
Llama 2
70B
37.5
71.9
63.6
69.4
35.2
68.9
51.2
54.2
Overall performance on grouped academic benchmarks.Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
TruthfulQA
Toxigen
Llama 1
7B
27.42
23.00
Llama 1
13B
41.74
23.08
Llama 1
33B
44.19
22.57
Llama 1
65B
48.71
21.77
Llama 2
7B
33.29
21.25
Llama 2
13B
41.86
26.10
Llama 2
70B
50.18
24.60
Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
TruthfulQA
Toxigen
Llama-2-Chat
7B
57.04
0.00
Llama-2-Chat
13B
62.18
0.00
Llama-2-Chat
70B
64.14
0.01
Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above.
Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
