Instructions to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="voidful/Llama-3.1-TAIDE-R1-8B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("voidful/Llama-3.1-TAIDE-R1-8B-Chat")
model = AutoModelForCausalLM.from_pretrained("voidful/Llama-3.1-TAIDE-R1-8B-Chat")

llama-cpp-python

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="voidful/Llama-3.1-TAIDE-R1-8B-Chat",
	filename="llama-3-1-TAIDE-R1-Chat.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat
# Run inference directly in the terminal:
llama-cli -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat
# Run inference directly in the terminal:
llama-cli -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

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 voidful/Llama-3.1-TAIDE-R1-8B-Chat
# Run inference directly in the terminal:
./llama-cli -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

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 voidful/Llama-3.1-TAIDE-R1-8B-Chat
# Run inference directly in the terminal:
./build/bin/llama-cli -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

Use Docker

docker model run hf.co/voidful/Llama-3.1-TAIDE-R1-8B-Chat

LM Studio
Jan

vLLM

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "voidful/Llama-3.1-TAIDE-R1-8B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "voidful/Llama-3.1-TAIDE-R1-8B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/voidful/Llama-3.1-TAIDE-R1-8B-Chat

SGLang

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "voidful/Llama-3.1-TAIDE-R1-8B-Chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "voidful/Llama-3.1-TAIDE-R1-8B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "voidful/Llama-3.1-TAIDE-R1-8B-Chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "voidful/Llama-3.1-TAIDE-R1-8B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Ollama:
```
ollama run hf.co/voidful/Llama-3.1-TAIDE-R1-8B-Chat
```

Unsloth Studio new

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat 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 voidful/Llama-3.1-TAIDE-R1-8B-Chat 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 voidful/Llama-3.1-TAIDE-R1-8B-Chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for voidful/Llama-3.1-TAIDE-R1-8B-Chat to start chatting

Pi new

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "voidful/Llama-3.1-TAIDE-R1-8B-Chat"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf voidful/Llama-3.1-TAIDE-R1-8B-Chat

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default voidful/Llama-3.1-TAIDE-R1-8B-Chat

Run Hermes

hermes

Docker Model Runner
How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Docker Model Runner:
```
docker model run hf.co/voidful/Llama-3.1-TAIDE-R1-8B-Chat
```

Lemonade

How to use voidful/Llama-3.1-TAIDE-R1-8B-Chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull voidful/Llama-3.1-TAIDE-R1-8B-Chat

Run and chat with the model

lemonade run user.Llama-3.1-TAIDE-R1-8B-Chat-{{QUANT_TAG}}

List all available models

lemonade list

voidful commited on Feb 24, 2025

Commit

fcf92c3

verified ·

1 Parent(s): 5ecc44d

Update template

Browse files

Files changed (1) hide show

template +53 -1

template CHANGED Viewed

@@ -10,4 +10,56 @@ SYSTEM """
 You first think about the reasoning process in the mind and then provide the user with the answer while reasoning step by step, and putting the final answer within \\boxed{}.
 The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e.,
 <think> reasoning process here </think><answer> answer here </answer>.
-"""

 You first think about the reasoning process in the mind and then provide the user with the answer while reasoning step by step, and putting the final answer within \\boxed{}.
 The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e.,
 <think> reasoning process here </think><answer> answer here </answer>.
+"""
+TEMPLATE """{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
+{{- if .System }}
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+Cutting Knowledge Date: December 2023
+When you receive a tool call response, use the output to format an answer to the orginal user question.
+You are a helpful assistant with tool calling capabilities.
+{{- end }}<|eot_id|>
+{{- end }}
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|><think>
+{{- if and $.Tools $last }}
+Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+{{ range $.Tools }}
+{{- . }}
+{{ end }}
+Question: {{ .Content }}<|eot_id|>
+{{- else }}
+{{ .Content }}<|eot_id|>
+{{- end }}{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+{{ end }}
+{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+{{- if .ToolCalls }}
+{{ range .ToolCalls }}
+{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+{{- else }}
+{{ .Content }}
+{{- end }}{{ if not $last }}<|eot_id|>{{ end }}
+{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+{{ end }}
+{{- end }}
+{{- end }}"""
+PARAMETER stop <|start_header_id|>
+PARAMETER stop <|end_header_id|>
+PARAMETER stop <|eot_id|>