Upload folder using huggingface_hub

.gitattributes

@@ -37,3 +37,21 @@ tinyfrank-f16.gguf filter=lfs diff=lfs merge=lfs -text
 tinyfrank-q2L.gguf filter=lfs diff=lfs merge=lfs -text
 tinyfrank-q4L.gguf filter=lfs diff=lfs merge=lfs -text
 tinyfrank-q6L.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q3_K.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q4_1.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q4_K.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q5_1.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q5_K.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tinyfrank-1.4B-f16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -58,7 +58,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: andrijdavid/tinyfrank-1.4B-GGUF and below it, a specific filename to download, such as: tinyfrank-1.4B.gguf.
+Under Download Model, you can enter the model repo: andrijdavid/tinyfrank-1.4B-GGUF and below it, a specific filename to download, such as: tinyfrank-1.4B-f16.gguf.
 
 Then click Download.
 
@@ -73,7 +73,7 @@ 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 andrijdavid/tinyfrank-1.4B-GGUF tinyfrank-1.4B.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download andrijdavid/tinyfrank-1.4B-GGUF tinyfrank-1.4B-f16.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -96,7 +96,7 @@ 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 andrijdavid/tinyfrank-1.4B-GGUF tinyfrank-1.4B.gguf --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download andrijdavid/tinyfrank-1.4B-GGUF tinyfrank-1.4B-f16.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.
@@ -108,7 +108,7 @@ 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 35 -m tinyfrank-1.4B.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
+./main -ngl 35 -m tinyfrank-1.4B-f16.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -159,7 +159,7 @@ pip install llama-cpp-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="./tinyfrank-1.4B.gguf",  # Download the model file first
+  model_path="./tinyfrank-1.4B-f16.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
@@ -172,7 +172,7 @@ output = llm(
   echo=True        # Whether to echo the prompt
 )
 # Chat Completion API
-llm = Llama(model_path="./tinyfrank-1.4B.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
+llm = Llama(model_path="./tinyfrank-1.4B-f16.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."},