Upload new GPTQs with varied parameters

README.md

@@ -1,14 +1,16 @@
 ---
-license: other
 inference: false
+license: other
+model_type: llama
 ---
+
 <!-- 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/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
+        <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>
@@ -18,129 +20,149 @@ inference: false
 
 # OpenAssistant LLaMA 30B SFT 7 GPTQ
 
-This in a repo of GPTQ format 4bit quantised models for [OpenAssistant's LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor).
+These files are GPTQ model files for [OpenAssistant LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor).
 
-It is the result of merging the XORs from the above repo with the original Llama 30B weights, and then quantising to 4bit GPU inference using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+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.
 
-This is epoch 7 of OpenAssistant's training of their Llama 30B model.
-
-**Please note that these models will need 24GB VRAM or greater to use effectively**
+These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
 
 ## Repositories available
 
-* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ).
-* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GGML).
-* [Unquantised 16bit model in HF format](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-HF).
-
-## PROMPT TEMPLATE
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GGML)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-HF)
 
-This model requires the following prompt template:
+## Prompt template: OpenAssistant
 
 ```
-<|prompter|> prompt goes here
-<|assistant|>:
+<|prompter|>{prompt}<|endoftext|><|assistant|>
 ```
 
-## CHOICE OF MODELS
-
-Three sets of models are provided:
+## Provided files
 
-* Groupsize = None
-  * Should work reliably in 24GB VRAM
-  * Uses --act-order for the best possible inference quality given its lack of group_size.
-* Groupsize = 1024
-  * Theoretically higher inference accuracy
-  * May OOM on long context lengths in 24GB VRAM
-* Groupsize = 128
-  * Optimal setting for highest inference quality
-  * Will definitely need more than 24GB VRAM on longer context lengths (1000-1500+ tokens returned)
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 
-For the 128g and 1024g models, two versions are available:
-* `compat.no-act-order.safetensor`
-  * Works with all versions of GPTQ-for-LLaMa, including the version in text-generation-webui one-click-installers
-* `latest.act-order.safetensors`
-  * uses `--act-order` for higher inference quality
-  * requires more recent GPTQ-for-LLaMa code, therefore will not currently work with one-click-installers
+Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
 
-## HOW TO CHOOSE YOUR MODEL
+| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
+| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
+| main | 4 | None | True | 16.94 GB | True | GPTQ-for-LLaMa | 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 | 19.44 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 | 18.18 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-128g-actorder_True | 4 | 128 | True | 17.55 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--1g-actorder_True | 8 | None | True | 32.99 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
+| gptq-8bit-128g-actorder_False | 8 | 128 | False | 33.73 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
+| gptq-3bit--1g-actorder_True | 3 | None | True | 12.92 GB | False | AutoGPTQ | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
+| gptq-3bit-128g-actorder_False | 3 | 128 | False | 13.51 GB | False | AutoGPTQ | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
 
-I have used branches to separate the models. This means you can clone the branch you want and not got model files you don't need.
+## How to download from branches
 
-If you have 24GB VRAM you are strongly recommended to use the file in `main`, with group_size = None.  This is fully compatible, and won't OOM.
-
-* Branch: **main** = groupsize None, `OpenAssistant-SFT-7-Llama-30B-GPTQ-4bit.safetensors` file
-* Branch: **1024-compat** = groupsize 1024, `compat.no-act-order.safetensors` file
-* Branch: **1024-latest** = groupsize 1024, `latest.act-order.safetensors` file
-* Branch: **128-compat** = groupsize 128, `compat.no-act-order.safetensors` file
-* Branch: **128-latest** = groupsize 128, `latest.act-order.safetensors` file
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/OpenAssistant-SFT-7-Llama-30B-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/OpenAssistant-SFT-7-Llama-30B-GPTQ`
+```
+- In Python Transformers code, the branch is the `revision` parameter; see below.
 
-![branches](https://i.imgur.com/PdiHnLxm.png)
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 
-## How to easily download and run the 1024g compat model in text-generation-webui
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 
-Open the text-generation-webui UI as normal.
+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/OpenAssistant-SFT-7-Llama-30B-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ:gptq-4bit-32g-actorder_True`
+  - see Provided Files above for the list of branches for each option.
 3. Click **Download**.
-4. Wait until it says it's finished downloading.
-5. Click the **Refresh** icon next to **Model** in the top left.
-6. In the **Model drop-down**: choose the model you just downloaded, `OpenAssistant-SFT-7-Llama-30B-GPTQ`.
-7. If you see an error in the bottom right, ignore it - it's temporary.
-8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = None`, `model_type = Llama`
-9. Click **Save settings for this model** in the top right.
-10. Click **Reload the Model** in the top right.
-11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
+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: `OpenAssistant-SFT-7-Llama-30B-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!
 
-## Manual instructions for `text-generation-webui`
+## How to use this GPTQ model from Python code
 
-The `compat.no-act-order.safetensors` files can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
 
-[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)).
+`GITHUB_ACTIONS=true pip install auto-gptq`
 
-The `latest.act-order.safetensors` files were created using `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI.
+Then try the following example code:
 
-If you want to use the act-order `safetensors` files and need to update the Triton branch of GPTQ-for-LLaMa, here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
-```
-# Clone text-generation-webui, if you don't already have it
-git clone https://github.com/oobabooga/text-generation-webui
-# Make a repositories directory
-mkdir text-generation-webui/repositories
-cd text-generation-webui/repositories
-# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
-```
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
 
-Then install this model into `text-generation-webui/models` and launch the UI as follows:
-```
-cd text-generation-webui
-python server.py --model OpenAssistant-SFT-7-Llama-30B-GPTQ --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
-```
+model_name_or_path = "TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ"
+model_basename = "OpenAssistant-SFT-7-Llama-30B-GPTQ-4bit--1g.act.order"
 
-To update the CUDA branch of GPTQ-for-LLaMa, you can do the following. **This requires a C/C++ compiler and the CUDA toolkit installed!**
-```
-# Clone text-generation-webui, if you don't already have it
-git clone https://github.com/oobabooga/text-generation-webui
-# Make a repositories directory
-mkdir text-generation-webui/repositories
-cd text-generation-webui/repositories
-# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui
-git clone -b cuda https://github.com/qwopqwop200/GPTQ-for-LLaMa
-cd GPTQ-for-LLaMa
-pip uninstall quant-cuda # uninstall existing CUDA version
-python setup_cuda.py install # install latest version
+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'''<|prompter|>{prompt}<|endoftext|><|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'])
 ```
 
-The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
+## 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.
 
-If you can't update GPTQ-for-LLaMa or don't want to, please use a `compat.no-act-order.safetensor` file.
+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/Jq4vkcDakD)
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
 
 ## Thanks, and how to contribute.
 
@@ -155,11 +177,158 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 * Patreon: https://patreon.com/TheBlokeAI
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
 
-**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+
+**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
 
 Thank you to all my generous patrons and donaters!
+
 <!-- footer end -->
-# Original model card
+
+# Original model card: OpenAssistant LLaMA 30B SFT 7
+
+
+# OpenAssistant LLaMA 30B SFT 7
+
+Due to the license attached to LLaMA models by Meta AI it is not possible to directly distribute LLaMA-based models. Instead we provide XOR weights for the OA models.
+
+Thanks to Mick for writing the `xor_codec.py` script which enables this process
+
+## The Process
+
+Note: This process applies to `oasst-sft-7-llama-30b` model. The same process can be applied to other models in future, but the checksums will be different..
+
+**This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.**
+
+To use OpenAssistant LLaMA-Based Models, you should have a copy of the original LLaMA model weights and add them to a `llama` subdirectory here. If you cannot obtain the original LLaMA, see the note in italic below for a possible alternative.
+
+Ensure your LLaMA 30B checkpoint matches the correct md5sums:
+
+```
+f856e9d99c30855d6ead4d00cc3a5573  consolidated.00.pth
+d9dbfbea61309dc1e087f5081e98331a  consolidated.01.pth
+2b2bed47912ceb828c0a37aac4b99073  consolidated.02.pth
+ea0405cdb5bc638fee12de614f729ebc  consolidated.03.pth
+4babdbd05b8923226a9e9622492054b6  params.json
+```
+
+*If you do not have a copy of the original LLaMA weights and cannot obtain one, you may still be able to complete this process. Some users have reported that [this model](https://huggingface.co/elinas/llama-30b-hf-transformers-4.29) can be used as a base for the XOR conversion. This will also allow you to skip to Step 7. However, we only support conversion starting from LLaMA original checkpoint and cannot provide support if you experience issues with this alternative approach.*
+
+**Important: Follow these exact steps to convert your original LLaMA checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.**
+
+1. Create a clean Python **3.10** virtual environment & activate it:
+
+```
+python3.10 -m venv xor_venv
+source xor_venv/bin/activate
+```
+
+2. Clone transformers repo and switch to tested version:
+
+```
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c
+pip install .
+```
+
+3. Install **exactly** these dependency versions:
+
+```
+pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1
+```
+
+4. Check `pip freeze` output:
+
+```
+accelerate==0.18.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+filelock==3.12.0
+huggingface-hub==0.13.4
+idna==3.4
+numpy==1.24.2
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+packaging==23.1
+protobuf==3.20.1
+psutil==5.9.5
+PyYAML==6.0
+regex==2023.3.23
+requests==2.28.2
+sentencepiece==0.1.98
+tokenizers==0.13.3
+torch==1.13.1
+tqdm==4.65.0
+transformers @ file:///mnt/data/koepf/transformers
+typing_extensions==4.5.0
+urllib3==1.26.15
+```
+
+5. While in `transformers` repo root, run HF LLaMA conversion script:
+
+```
+python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir <input_path_llama_base>  --output_dir <output_path_llama30b_hf> --model_size 30B
+```
+
+6. Run `find . -type f -exec md5sum "{}" +` in the conversion target directory (`output_dir`). This should produce exactly the following checksums if your files are correct:
+
+```
+462a2d07f65776f27c0facfa2affb9f9  ./pytorch_model-00007-of-00007.bin
+e1dc8c48a65279fb1fbccff14562e6a3  ./pytorch_model-00003-of-00007.bin
+9cffb1aeba11b16da84b56abb773d099  ./pytorch_model-00001-of-00007.bin
+aee09e21813368c49baaece120125ae3  ./generation_config.json
+92754d6c6f291819ffc3dfcaf470f541  ./pytorch_model-00005-of-00007.bin
+3eddc6fc02c0172d38727e5826181adb  ./pytorch_model-00004-of-00007.bin
+eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
+99762d59efa6b96599e863893cf2da02  ./pytorch_model-00006-of-00007.bin
+598538f18fed1877b41f77de034c0c8a  ./config.json
+fdb311c39b8659a5d5c1991339bafc09  ./tokenizer.json
+fecfda4fba7bfd911e187a85db5fa2ef  ./pytorch_model.bin.index.json
+edd1a5897748864768b1fab645b31491  ./tokenizer_config.json
+6b2e0a735969660e720c27061ef3f3d3  ./special_tokens_map.json
+5cfcb78b908ffa02e681cce69dbe4303  ./pytorch_model-00002-of-00007.bin
+```
+
+**Important: You should now have the correct LLaMA weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.**
+
+7. Once you have LLaMA weights in the correct format, you can apply the XOR decoding:
+
+```
+python xor_codec.py oasst-sft-7-llama-30b/ oasst-sft-7-llama-30b-xor/ llama30b_hf/
+```
+
+You should **expect to see one warning message** during execution:
+
+`Exception when processing 'added_tokens.json'`
+
+This is normal. **If similar messages appear for other files, something has gone wrong**.
+
+8. Now run `find . -type f -exec md5sum "{}" +` in the output directory (here `oasst-sft-6-llama-30b`). You should get a file with exactly these checksums:
+
+```
+8ae4537c64a1ef202d1d82eb0d356703  ./pytorch_model-00007-of-00007.bin
+d84f99d23369e159e50cb0597b6c9673  ./pytorch_model-00003-of-00007.bin
+f7de50a725d678eb65cc3dced727842f  ./pytorch_model-00001-of-00007.bin
+27b0dc092f99aa2efaf467b2d8026c3f  ./added_tokens.json
+aee09e21813368c49baaece120125ae3  ./generation_config.json
+31a2b04b139f4af043ad04478f1497f5  ./pytorch_model-00005-of-00007.bin
+a16a2dfacbde77a1659a7c9df7966d0a  ./pytorch_model-00004-of-00007.bin
+eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
+baa778a8679d47b085446faf97b72758  ./pytorch_model-00006-of-00007.bin
+b2d64f2198ab7b53e3b8d12fbcadeb3c  ./config.json
+deb33dd4ffc3d2baddcce275a00b7c1b  ./tokenizer.json
+76d47e4f51a8df1d703c6f594981fcab  ./pytorch_model.bin.index.json
+ed59bfee4e87b9193fea5897d610ab24  ./tokenizer_config.json
+704373f0c0d62be75e5f7d41d39a7e57  ./special_tokens_map.json
+e836168cdbbb74db51d04f25ed6408ce  ./pytorch_model-00002-of-00007.bin
+```
+
+If so you have successfully decoded the weights and should be able to use the model with HuggingFace Transformers. **If your checksums do not match those above, there is a problem.**
+
+### Configuration
 
 ```
 llama-30b-sft-7: