Instructions to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Raydev/talkie-1930-13b-yarn32k-tf-GGUF",
	filename="talkie-1930-13b-yarn32k-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raydev/talkie-1930-13b-yarn32k-tf-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raydev/talkie-1930-13b-yarn32k-tf-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Ollama
How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with Ollama:
```
ollama run hf.co/Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
```

Unsloth Studio

How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Raydev/talkie-1930-13b-yarn32k-tf-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Raydev/talkie-1930-13b-yarn32k-tf-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Raydev/talkie-1930-13b-yarn32k-tf-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with Docker Model Runner:
```
docker model run hf.co/Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M
```

Lemonade

How to use Raydev/talkie-1930-13b-yarn32k-tf-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Raydev/talkie-1930-13b-yarn32k-tf-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.talkie-1930-13b-yarn32k-tf-GGUF-Q4_K_M

List all available models

lemonade list

This repo contains the GGUF and quantized version of talkie-1930-13b-yarn-32k-tf It is based off of the base version of Talkie-1930, and therefore is not meant for actual chat conversations.

It is recommended to use llama.cpp to load this model.

Included files:

Quant File	Size
talkie-1930-13b-yarn32k-Q2_K.gguf	5.1G
talkie-1930-13b-yarn32k-Q3_K_S.gguf	5.8G
talkie-1930-13b-yarn32k-Q3_K_M.gguf	6.4G
talkie-1930-13b-yarn32k-Q4_K_S.gguf	7.4G
talkie-1930-13b-yarn32k-Q4_K_M.gguf	8.0G
talkie-1930-13b-yarn32k-Q5_K_S-ffn5_0.gguf	9.2G
talkie-1930-13b-yarn32k-Q5_K_M-ffn5_0.gguf	9.2G
talkie-1930-13b-yarn32k-Q6_K.gguf	11G
talkie-1930-13b-yarn32k-Q8_0.gguf	14G
talkie-1930-13b-yarn32k-f16.gguf	25G

Note:

ffn5_0 refer to those files having their ffn_down forced to be q5_0 rather than letting the usually determined 5_1, this has to do with the fact that Talkie's ffn_down is not divisble by 256 meaning the base llama-quantize chooses 5_1, although when I first quantized with 5_1, the models output were completely broken; repeating the same character over and over rather than generating text, irregardless of input. I am pretty sure the fact the ffn_down cannot be divided by 256 is just a architectural design decision made by the upstream talkie-1930-base model.

i have found this issue with the talkie-1930-13b-it-hf-GGUF models as well? Not necessarily the q5_1 issue, as I haven't verified it yet. But the exact mode of failure with a repeating '>' token or other control token with their Q5_K_M & K_S models.

Downloads last month: 454

GGUF

Model size

13B params

Architecture

talkie

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Raydev/talkie-1930-13b-yarn32k-tf-GGUF

Base model

talkie-lm/talkie-1930-13b-base

Finetuned

xlr8harder/talkie-1930-13b-base-tf

Finetuned

xlr8harder/talkie-1930-13b-yarn-32k-tf

Quantized

(1)

this model