Upload README.md

README.md

@@ -1,11 +1,11 @@
 ---
-base_model: migtissera/Tess-Medium-200K-v1.0
+base_model: migtissera/Tess-M-Creative-v1.0
 inference: false
 license: other
 license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
 license_name: yi-34b
 model_creator: Migel Tissera
-model_name: Tess Medium 200K v1.0
+model_name: Tess M Creative v1.0
 model_type: yi
 prompt_template: 'SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack
   when necessary to construct a clear, cohesive Chain of Thought reasoning. Always
@@ -37,14 +37,14 @@ quantized_by: TheBloke
 <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
 
-# Tess Medium 200K v1.0 - GGUF
+# Tess M Creative v1.0 - GGUF
 - Model creator: [Migel Tissera](https://huggingface.co/migtissera)
-- Original model: [Tess Medium 200K v1.0](https://huggingface.co/migtissera/Tess-Medium-200K-v1.0)
+- Original model: [Tess M Creative v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0)
 
 <!-- description start -->
 ## Description
 
-This repo contains GGUF format model files for [Migel Tissera's Tess Medium 200K v1.0](https://huggingface.co/migtissera/Tess-Medium-200K-v1.0).
+This repo contains GGUF format model files for [Migel Tissera's Tess M Creative v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0).
 
 These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
 
@@ -70,10 +70,10 @@ Here is an incomplete list of clients and libraries that are known to support GG
 <!-- repositories-available start -->
 ## Repositories available
 
-* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-AWQ)
-* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF)
-* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Tess-Medium-200K-v1.0)
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF)
+* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Tess-M-Creative-v1.0)
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
@@ -118,18 +118,18 @@ Refer to the Provided Files table below to see what files use which methods, and
 
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| [tess-medium-200k-v1.0.Q2_K.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q2_K.gguf) | Q2_K | 2 | 14.56 GB| 17.06 GB | smallest, significant quality loss - not recommended for most purposes |
-| [tess-medium-200k-v1.0.Q3_K_S.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
-| [tess-medium-200k-v1.0.Q3_K_M.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 16.64 GB| 19.14 GB | very small, high quality loss |
-| [tess-medium-200k-v1.0.Q3_K_L.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
-| [tess-medium-200k-v1.0.Q4_0.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [tess-medium-200k-v1.0.Q4_K_S.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 19.54 GB| 22.04 GB | small, greater quality loss |
-| [tess-medium-200k-v1.0.Q4_K_M.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
-| [tess-medium-200k-v1.0.Q5_0.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [tess-medium-200k-v1.0.Q5_K_S.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
-| [tess-medium-200k-v1.0.Q5_K_M.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
-| [tess-medium-200k-v1.0.Q6_K.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
-| [tess-medium-200k-v1.0.Q8_0.gguf](https://huggingface.co/TheBloke/Tess-Medium-200K-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 GB | very large, extremely low quality loss - not recommended |
+| [tess-medium-200k-v1.0.Q2_K.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q2_K.gguf) | Q2_K | 2 | 14.56 GB| 17.06 GB | smallest, significant quality loss - not recommended for most purposes |
+| [tess-medium-200k-v1.0.Q3_K_S.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
+| [tess-medium-200k-v1.0.Q3_K_M.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 16.64 GB| 19.14 GB | very small, high quality loss |
+| [tess-medium-200k-v1.0.Q3_K_L.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
+| [tess-medium-200k-v1.0.Q4_0.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [tess-medium-200k-v1.0.Q4_K_S.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 19.54 GB| 22.04 GB | small, greater quality loss |
+| [tess-medium-200k-v1.0.Q4_K_M.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
+| [tess-medium-200k-v1.0.Q5_0.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [tess-medium-200k-v1.0.Q5_K_S.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
+| [tess-medium-200k-v1.0.Q5_K_M.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
+| [tess-medium-200k-v1.0.Q6_K.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
+| [tess-medium-200k-v1.0.Q8_0.gguf](https://huggingface.co/TheBloke/Tess-M-Creative-v1.0-GGUF/blob/main/tess-medium-200k-v1.0.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 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.
 
@@ -150,7 +150,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: TheBloke/Tess-Medium-200K-v1.0-GGUF and below it, a specific filename to download, such as: tess-medium-200k-v1.0.Q4_K_M.gguf.
+Under Download Model, you can enter the model repo: TheBloke/Tess-M-Creative-v1.0-GGUF and below it, a specific filename to download, such as: tess-m-creative-v1.0.Q4_K_M.gguf.
 
 Then click Download.
 
@@ -165,7 +165,7 @@ 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 TheBloke/Tess-Medium-200K-v1.0-GGUF tess-medium-200k-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download TheBloke/Tess-M-Creative-v1.0-GGUF tess-m-creative-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -174,7 +174,7 @@ huggingface-cli download TheBloke/Tess-Medium-200K-v1.0-GGUF tess-medium-200k-v1
 You can also download multiple files at once with a pattern:
 
 ```shell
-huggingface-cli download TheBloke/Tess-Medium-200K-v1.0-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
+huggingface-cli download TheBloke/Tess-M-Creative-v1.0-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).
@@ -188,7 +188,7 @@ 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 TheBloke/Tess-Medium-200K-v1.0-GGUF tess-medium-200k-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Tess-M-Creative-v1.0-GGUF tess-m-creative-v1.0.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.
@@ -201,7 +201,7 @@ Windows Command Line users: You can set the environment variable by running `set
 Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 32 -m tess-medium-200k-v1.0.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.\nUSER: {prompt}\nASSISTANT:"
+./main -ngl 32 -m tess-m-creative-v1.0.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.\nUSER: {prompt}\nASSISTANT:"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -243,7 +243,7 @@ CT_METAL=1 pip install ctransformers --no-binary ctransformers
 from ctransformers import AutoModelForCausalLM
 
 # 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 = AutoModelForCausalLM.from_pretrained("TheBloke/Tess-Medium-200K-v1.0-GGUF", model_file="tess-medium-200k-v1.0.Q4_K_M.gguf", model_type="yi", gpu_layers=50)
+llm = AutoModelForCausalLM.from_pretrained("TheBloke/Tess-M-Creative-v1.0-GGUF", model_file="tess-m-creative-v1.0.Q4_K_M.gguf", model_type="yi", gpu_layers=50)
 
 print(llm("AI is going to"))
 ```
@@ -292,31 +292,25 @@ And thank you again to a16z for their generous grant.
 <!-- footer end -->
 
 <!-- original-model-card start -->
-# Original model card: Migel Tissera's Tess Medium 200K v1.0
+# Original model card: Migel Tissera's Tess M Creative v1.0
 
 
 # Tess
 
-![Tess](https://huggingface.co/migtissera/Tess-XL-v1.0/resolve/main/Tess.png)
+![Tess](https://huggingface.co/migtissera/Tess-M-v1.0/resolve/main/Tess.png)
 
-Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-XS is trained on the Mistral-7B base.
+Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-M series is trained on the Yi-34B-200K base.
+
+Tess-M-Creative is an AI most suited for creative tasks, such as writing, role play, design and exploring novel concepts. While it has been trained on STEM, its reasoning capabilities may lag state-of-the-art. Please download Tess-M-STEM series for reasoning, logic and STEM related tasks.
 
 
 # Prompt Format:
 
 ```
-SYSTEM:
+SYSTEM: <ANY SYSTEM CONTEXT>
 USER: What is the relationship between Earth's atmosphere, magnetic field and gravity?
 ASSISTANT:
 ```
 
-# Synthia-CoT Format:
-Tess also supports Synthia-CoT format:
-
-```
-SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
-USER:
-ASSISTANT:
-```
 
 <!-- original-model-card end -->