Update README.md

README.md

@@ -2,11 +2,11 @@
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 inference: false
 license: apache-2.0
-model_creator: Mistral AI_
-model_name: Mistral 7B Instruct v0.2
-model_type: mistral
+model_creator: L-R/_
+model_name: LLmRA-3B-v0.1
+model_type: llama2
 pipeline_tag: text-generation
-prompt_template: '<s>[INST] {prompt} [/INST]
+prompt_template: '<|system|>, <|user|>, <|model|>.
 
   '
 quantized_by: TheBlock
@@ -36,13 +36,13 @@ tags:
 <!-- header end -->
 
 # Mistral 7B Instruct v0.2 - GGUF
-- Model creator: [Mistral AI_](https://huggingface.co/mistralai)
-- Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- Model creator: [LinguaRole Labs](https://huggingface.co/L-R/)
+- Original model: [LLmRA-3B-v0.1](https://huggingface.co/L-R/LLmRA-3B-v0.1)
 
 <!-- description start -->
 ## Description
 
-This repo contains GGUF format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
+This repo contains GGUF format model files for [LinguaRole Labs](https://huggingface.co/L-R/LLmRA-3B-v0.1).
 
 These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
 
@@ -69,15 +69,15 @@ Here is an incomplete list of clients and libraries that are known to support GG
 <!-- repositories-available start -->
 ## Repositories available
 
-* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF)
-* [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](TheBlock/LLmRA-3B-v0.1-GGUF)
+* [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/L-R/LLmRA-3B-v0.1)
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
-## Prompt template: Mistral
+## Prompt template: Custom
 
 ```
-<s>[INST] {prompt} [/INST]
+<|system|>, <|user|>, <|model|>.
 
 ```
 
@@ -91,43 +91,6 @@ These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwa
 
 They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
 
-## Explanation of quantisation methods
-
-<details>
-  <summary>Click to see details</summary>
-
-The new methods available are:
-
-* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
-* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
-* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
-* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
-* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
-
-Refer to the Provided Files table below to see what files use which methods, and how.
-</details>
-<!-- compatibility_gguf end -->
-
-<!-- README_GGUF.md-provided-files start -->
-## Provided files
-
-| Name | Quant method | Bits | Size | Max RAM required | Use case |
-| ---- | ---- | ---- | ---- | ---- | ----- |
-| [mistral-7b-instruct-v0.2.Q2_K.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
-| [mistral-7b-instruct-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
-| [mistral-7b-instruct-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
-| [mistral-7b-instruct-v0.2.Q3_K_L.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
-| [mistral-7b-instruct-v0.2.Q4_0.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [mistral-7b-instruct-v0.2.Q4_K_S.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
-| [mistral-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
-| [mistral-7b-instruct-v0.2.Q5_0.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [mistral-7b-instruct-v0.2.Q5_K_S.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
-| [mistral-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
-| [mistral-7b-instruct-v0.2.Q6_K.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
-| [mistral-7b-instruct-v0.2.Q8_0.gguf](https://huggingface.co/TheBlock/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
-
-**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
-
 
 
 <!-- README_GGUF.md-provided-files end -->
@@ -145,7 +108,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: TheBlock/Mistral-7B-Instruct-v0.2-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.gguf.
+Under Download Model, you can enter the model repo: TheBlock/LLmRA-3B-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.gguf.
 
 Then click Download.
 
@@ -160,140 +123,9 @@ pip3 install huggingface-hub
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 
 ```shell
-huggingface-cli download TheBlock/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
-```
-
-<details>
-  <summary>More advanced huggingface-cli download usage (click to read)</summary>
-
-You can also download multiple files at once with a pattern:
-
-```shell
-huggingface-cli download TheBlock/Mistral-7B-Instruct-v0.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
-```
-
-For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
-
-To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
-
-```shell
-pip3 install hf_transfer
-```
-
-And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
-
-```shell
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBlock/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
-```
-
-Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
-</details>
-<!-- README_GGUF.md-how-to-download end -->
-
-<!-- README_GGUF.md-how-to-run start -->
-## Example `llama.cpp` command
-
-Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
-
-```shell
-./main -ngl 35 -m mistral-7b-instruct-v0.2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"
-```
-
-Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
-
-Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
-
-If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
-
-For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
-
-## How to run in `text-generation-webui`
-
-Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
-
-## How to run from Python code
-
-You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
-
-### How to load this model in Python code, using llama-cpp-python
-
-For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
-
-#### First install the package
-
-Run one of the following commands, according to your system:
-
-```shell
-# Base ctransformers with no GPU acceleration
-pip install llama-cpp-python
-# With NVidia CUDA acceleration
-CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
-# Or with OpenBLAS acceleration
-CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
-# Or with CLBLast acceleration
-CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
-# Or with AMD ROCm GPU acceleration (Linux only)
-CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
-# Or with Metal GPU acceleration for macOS systems only
-CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
-
-# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
-$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
-pip install llama-cpp-python
-```
-
-#### Simple llama-cpp-python example code
-
-```python
-from llama_cpp import Llama
-
-# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
-llm = Llama(
-  model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf",  # Download the model file first
-  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
-  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
-  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
-)
-
-# Simple inference example
-output = llm(
-  "<s>[INST] {prompt} [/INST]", # Prompt
-  max_tokens=512,  # Generate up to 512 tokens
-  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
-  echo=True        # Whether to echo the prompt
-)
-
-# Chat Completion API
-
-llm = Llama(model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
-llm.create_chat_completion(
-    messages = [
-        {"role": "system", "content": "You are a story writing assistant."},
-        {
-            "role": "user",
-            "content": "Write a story about llamas."
-        }
-    ]
-)
+huggingface-cli download TheBlock/LLmRA-3B-v0.1-GGUF LLmRA-3B-v0.1-q8_0.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
-## How to use with LangChain
-
-Here are guides on using llama-cpp-python and ctransformers with LangChain:
-
-* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
-* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
-
-<!-- README_GGUF.md-how-to-run end -->
-
-<!-- footer start -->
-<!-- 200823 -->
-## Discord
-
-For further support, and discussions on these models and AI in general, join us at:
-
-[TheBlock AI's Discord server](https://discord.gg/TheBlokeai)
-
 ## Thanks, and how to contribute
 
 Thanks to the [chirper.ai](https://chirper.ai) team!
@@ -321,85 +153,8 @@ And thank you again to a16z for their generous grant.
 <!-- footer end -->
 
 <!-- original-model-card start -->
-# Original model card: Mistral AI_'s Mistral 7B Instruct v0.2
-
-
-# Model Card for Mistral-7B-Instruct-v0.2
-
-The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
-
-For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
-
-## Instruction format
-
-In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
-
-E.g.
-```
-text = "<s>[INST] What is your favourite condiment? [/INST]"
-"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
-"[INST] Do you have mayonnaise recipes? [/INST]"
-```
-
-This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-device = "cuda" # the device to load the model onto
-
-model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
-
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
-
-encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
-
-model_inputs = encodeds.to(device)
-model.to(device)
-
-generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
-decoded = tokenizer.batch_decode(generated_ids)
-print(decoded[0])
-```
-
-## Model Architecture
-This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
-- Grouped-Query Attention
-- Sliding-Window Attention
-- Byte-fallback BPE tokenizer
-
-## Troubleshooting
-- If you see the following error:
-```
-Traceback (most recent call last):
-File "", line 1, in
-File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
-config, kwargs = AutoConfig.from_pretrained(
-File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
-config_class = CONFIG_MAPPING[config_dict["model_type"]]
-File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
-raise KeyError(key)
-KeyError: 'mistral'
-```
-
-Installing transformers from source should solve the issue
-pip install git+https://github.com/huggingface/transformers
-
-This should not be required after transformers-v4.33.4.
-
-## Limitations
-
-The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
-It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
-make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
-
-## The Mistral AI Team
+# Original model card: L-R/LLmRA-3B-v0.1
 
-Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
 
+https://huggingface.co/L-R/LLmRA-3B-v0.1
 <!-- original-model-card end -->