Instructions to use maidacundo/panda_agi_64_8_batch_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maidacundo/panda_agi_64_8_batch_gguf with Transformers:

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

pipe = pipeline("text-generation", model="maidacundo/panda_agi_64_8_batch_gguf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maidacundo/panda_agi_64_8_batch_gguf")
model = AutoModelForCausalLM.from_pretrained("maidacundo/panda_agi_64_8_batch_gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use maidacundo/panda_agi_64_8_batch_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 maidacundo/panda_agi_64_8_batch_gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf maidacundo/panda_agi_64_8_batch_gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf maidacundo/panda_agi_64_8_batch_gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf maidacundo/panda_agi_64_8_batch_gguf:Q8_0

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 maidacundo/panda_agi_64_8_batch_gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf maidacundo/panda_agi_64_8_batch_gguf:Q8_0

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 maidacundo/panda_agi_64_8_batch_gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maidacundo/panda_agi_64_8_batch_gguf:Q8_0

Use Docker

docker model run hf.co/maidacundo/panda_agi_64_8_batch_gguf:Q8_0

LM Studio
Jan

vLLM

How to use maidacundo/panda_agi_64_8_batch_gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maidacundo/panda_agi_64_8_batch_gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maidacundo/panda_agi_64_8_batch_gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maidacundo/panda_agi_64_8_batch_gguf:Q8_0

SGLang

How to use maidacundo/panda_agi_64_8_batch_gguf 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 "maidacundo/panda_agi_64_8_batch_gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maidacundo/panda_agi_64_8_batch_gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "maidacundo/panda_agi_64_8_batch_gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maidacundo/panda_agi_64_8_batch_gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use maidacundo/panda_agi_64_8_batch_gguf with Ollama:
```
ollama run hf.co/maidacundo/panda_agi_64_8_batch_gguf:Q8_0
```

Unsloth Studio

How to use maidacundo/panda_agi_64_8_batch_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 maidacundo/panda_agi_64_8_batch_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 maidacundo/panda_agi_64_8_batch_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maidacundo/panda_agi_64_8_batch_gguf to start chatting

Docker Model Runner
How to use maidacundo/panda_agi_64_8_batch_gguf with Docker Model Runner:
```
docker model run hf.co/maidacundo/panda_agi_64_8_batch_gguf:Q8_0
```

Lemonade

How to use maidacundo/panda_agi_64_8_batch_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maidacundo/panda_agi_64_8_batch_gguf:Q8_0

Run and chat with the model

lemonade run user.panda_agi_64_8_batch_gguf-Q8_0

List all available models

lemonade list

Atomic Chat

panda_agi_64_8_batch_gguf / template

maidacundo

Create template

6611040 verified about 1 year ago

Raw

History Blame Contribute Delete

1.48 kB

	{{- if .Messages }}
	{{- if or .System .Tools }}<\|im_start\|>system
	{{- if .System }}
	{{ .System }}
	{{- end }}
	{{- if .Tools }}

	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{- range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{- end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call>
	{{- end }}<\|im_end\|>
	{{ end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1 -}}
	{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if .Content }}{{ .Content }}
	{{- else if .ToolCalls }}<tool_call>
	{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	{{ end }}</tool_call>
	{{- end }}{{ if not $last }}<\|im_end\|>
	{{ end }}
	{{- else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{ end }}
	{{- if and (ne .Role "assistant") $last }}<\|im_start\|>assistant
	{{ end }}
	{{- end }}
	{{- else }}
	{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{ if .Prompt }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	{{ end }}<\|im_start\|>assistant
	{{ end }}{{ .Response }}{{ if .Response }}<\|im_end\|>{{ end }}