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LICENSE.txt

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+LLAMA 2 COMMUNITY LICENSE AGREEMENT	
+Llama 2 Version Release Date: July 18, 2023
+
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+

Notice.txt

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+Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

README.md

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+---
+inference: false
+language:
+- en
+license: other
+model_type: llama
+tags:
+- llama-2
+- self-instruct
+- distillation
+- synthetic instruction
+---
+
+<!-- header start -->
+<div style="width: 100%;">
+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<!-- header end -->
+
+# Nous Research's Nous Hermes Llama 2 13B GPTQ
+
+These files are GPTQ model files for [Nous Research's Nous Hermes Llama 2 13B](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b).
+
+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.
+
+
+## Repositories available
+
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GGML)
+* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b)
+
+## Prompt template: Alpaca
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction: {prompt}
+
+### Response:
+```
+
+## 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 | 7.26 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 | 8.00 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 | 7.51 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 | 7.26 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. | 
+| gptq-8bit-128g-actorder_True | 8 | 128 | True | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. | 
+| gptq-8bit-64g-actorder_True | 8 | 64 | True | 13.95 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. | 
+| gptq-8bit-128g-actorder_False | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | 
+| gptq-8bit--1g-actorder_True | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
+
+## How to download from branches
+
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Nous-Hermes-Llama2-GPTQ:gptq-4bit-32g-actorder_True`
+- With Git, you can clone a branch with:
+```
+git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GPTQ`
+```
+- In Python Transformers code, the branch is the `revision` parameter; see below.
+
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/Nous-Hermes-Llama2-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/Nous-Hermes-Llama2-GPTQ:gptq-4bit-32g-actorder_True`
+  - see Provided Files above for the list of branches for each option.
+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: `Nous-Hermes-Llama2-GPTQ`
+7. The model will automatically load, and is now ready for use!
+8. 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`.
+9. 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](https://github.com/PanQiWei/AutoGPTQ) installed:
+
+`GITHUB_ACTIONS=true pip install auto-gptq`
+
+Then try the following example code:
+
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+model_name_or_path = "TheBloke/Nous-Hermes-Llama2-GPTQ"
+model_basename = "gptq_model-4bit-128g"
+
+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)
+
+"""
+To download from a specific branch, use the revision parameter, as in this example:
+
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        revision="gptq-4bit-32g-actorder_True",
+        model_basename=model_basename,
+        use_safetensors=True,
+        trust_remote_code=False,
+        device="cuda:0",
+        quantize_config=None)
+"""
+
+prompt = "Tell me about AI"
+prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction: {prompt}
+
+### Response:
+'''
+
+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'])
+```
+
+## 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.
+
+<!-- footer start -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+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.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+
+**Patreon special mentions**: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse
+
+
+Thank you to all my generous patrons and donaters!
+
+<!-- footer end -->
+
+# Original model card: Nous Research's Nous Hermes Llama 2 13B
+
+
+# Model Card: Nous-Hermes-Llama2-13b
+
+Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.
+
+## Model Description
+
+Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.
+
+This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.
+
+This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.
+
+## Example Outputs:
+![Example4](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example5.png "Example 4")
+![Example1](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/Example1.png "Example 1")
+![Example2](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example2.png "Example 2")
+![Example3](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example3.png "Example 3")
+
+## Model Training
+
+The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.
+
+This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
+
+## Collaborators
+The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. 
+  
+Special mention goes to @winglian for assisting in some of the training issues.
+
+Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. 
+
+Among the contributors of datasets:
+- GPTeacher was made available by Teknium
+- Wizard LM by nlpxucan
+- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.  
+- GPT4-LLM and Unnatural Instructions were provided by Microsoft
+- Airoboros dataset by jondurbin
+- Camel-AI's domain expert datasets are from Camel-AI
+- CodeAlpaca dataset by Sahil 2801.
+
+If anyone was left out, please open a thread in the community tab.
+
+## Prompt Format
+
+The model follows the Alpaca prompt format:
+```
+### Instruction:
+<prompt>
+
+### Response:
+<leave a newline blank for model to respond>
+
+```
+
+or 
+
+```
+### Instruction:
+<prompt>
+
+### Input:
+<additional context>
+
+### Response:
+<leave a newline blank for model to respond>
+
+```  
+
+## Benchmark Results
+AGI-Eval
+```
+|             Task             |Version| Metric |Value |   |Stderr|
+|agieval_aqua_rat              |      0|acc     |0.2362|±  |0.0267|
+|                              |       |acc_norm|0.2480|±  |0.0272|
+|agieval_logiqa_en             |      0|acc     |0.3425|±  |0.0186|
+|                              |       |acc_norm|0.3472|±  |0.0187|
+|agieval_lsat_ar               |      0|acc     |0.2522|±  |0.0287|
+|                              |       |acc_norm|0.2087|±  |0.0269|
+|agieval_lsat_lr               |      0|acc     |0.3510|±  |0.0212|
+|                              |       |acc_norm|0.3627|±  |0.0213|
+|agieval_lsat_rc               |      0|acc     |0.4647|±  |0.0305|
+|                              |       |acc_norm|0.4424|±  |0.0303|
+|agieval_sat_en                |      0|acc     |0.6602|±  |0.0331|
+|                              |       |acc_norm|0.6165|±  |0.0340|
+|agieval_sat_en_without_passage|      0|acc     |0.4320|±  |0.0346|
+|                              |       |acc_norm|0.4272|±  |0.0345|
+|agieval_sat_math              |      0|acc     |0.2909|±  |0.0307|
+|                              |       |acc_norm|0.2727|±  |0.0301|
+```
+GPT-4All Benchmark Set
+```
+|    Task     |Version| Metric |Value |   |Stderr|
+|arc_challenge|      0|acc     |0.5102|±  |0.0146|
+|             |       |acc_norm|0.5213|±  |0.0146|
+|arc_easy     |      0|acc     |0.7959|±  |0.0083|
+|             |       |acc_norm|0.7567|±  |0.0088|
+|boolq        |      1|acc     |0.8394|±  |0.0064|
+|hellaswag    |      0|acc     |0.6164|±  |0.0049|
+|             |       |acc_norm|0.8009|±  |0.0040|
+|openbookqa   |      0|acc     |0.3580|±  |0.0215|
+|             |       |acc_norm|0.4620|±  |0.0223|
+|piqa         |      0|acc     |0.7992|±  |0.0093|
+|             |       |acc_norm|0.8069|±  |0.0092|
+|winogrande   |      0|acc     |0.7127|±  |0.0127|
+```
+BigBench Reasoning Test
+```
+|                      Task                      |Version|       Metric        |Value |   |Stderr|
+
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5526|±  |0.0362|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7344|±  |0.0230|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.2636|±  |0.0275|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.0195|±  |0.0073|
+|                                                |       |exact_str_match      |0.0000|±  |0.0000|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2760|±  |0.0200|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2100|±  |0.0154|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4400|±  |0.0287|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2440|±  |0.0192|
+|bigbench_navigate                               |      0|multiple_choice_grade|0.4950|±  |0.0158|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5570|±  |0.0111|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|0.3728|±  |0.0229|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1854|±  |0.0123|
+|bigbench_snarks                                 |      0|multiple_choice_grade|0.6298|±  |0.0360|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.6156|±  |0.0155|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3140|±  |0.0147|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2032|±  |0.0114|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1406|±  |0.0083|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4400|±  |0.0287|
+```
+
+These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:
+- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1
+- 0.3657 on BigBench, up from 0.328 on hermes-llama1
+- 0.372 on AGIEval, up from 0.354 on Hermes-llama1
+
+These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position. 
+
+## Resources for Applied Use Cases:
+For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord  
+For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot  
+
+## Future Plans
+We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. 
+
+## Model Usage
+The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
+  

USE_POLICY.md

@@ -0,0 +1,50 @@
+# Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
+
+## Prohibited Uses
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
+
+1. Violate the law or others’ rights, including to:
+    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
+        1. Violence or terrorism 
+        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+        3. Human trafficking, exploitation, and sexual violence
+        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+        5. Sexual solicitation
+        6. Any other criminal activity
+    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
+    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
+
+
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+    2. Guns and illegal weapons (including weapon development)
+    3. Illegal drugs and regulated/controlled substances
+    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+    3. Generating, promoting, or further distributing spam
+    4. Impersonating another individual without consent, authorization, or legal right
+    5. Representing that the use of Llama 2 or outputs are human-generated
+    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+4. Fail to appropriately disclose to end users any known dangers of your AI system 
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])
+

added_tokens.json

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+}

config.json

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+{
+    "_name_or_path": "output/hermes-llama2-4k/checkpoint-2259",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
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+    "initializer_range": 0.02,
+    "intermediate_size": 13824,
+    "max_position_embeddings": 4096,
+    "model_type": "llama",
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+    "num_hidden_layers": 40,
+    "num_key_value_heads": 40,
+    "pad_token_id": 0,
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+    "rms_norm_eps": 1e-05,
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+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.32.0.dev0",
+    "use_cache": true,
+    "vocab_size": 32032
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "temperature": 0.9,
+  "top_p": 0.6,
+  "transformers_version": "4.32.0.dev0"
+}

gptq_model-8bit-128g.safetensors

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+size 13653553504

quantize_config.json

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+  "bits": 8,
+  "group_size": 128,
+  "damp_percent": 0.01,
+  "desc_act": true,
+  "sym": true,
+  "true_sequential": true,
+  "model_name_or_path": null,
+  "model_file_base_name": null
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 499723

tokenizer_config.json

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+{
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}