Instructions to use g023/qwen3-tiny-v2-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use g023/qwen3-tiny-v2-finetuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="g023/qwen3-tiny-v2-finetuned",
	filename="Qwen3-g023-tiny-v2-FT-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use g023/qwen3-tiny-v2-finetuned with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2-finetuned:Q8_0
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2-finetuned:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2-finetuned:Q8_0
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2-finetuned: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 g023/qwen3-tiny-v2-finetuned:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf g023/qwen3-tiny-v2-finetuned: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 g023/qwen3-tiny-v2-finetuned:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf g023/qwen3-tiny-v2-finetuned:Q8_0

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2-finetuned:Q8_0

LM Studio
Jan

vLLM

How to use g023/qwen3-tiny-v2-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "g023/qwen3-tiny-v2-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "g023/qwen3-tiny-v2-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2-finetuned:Q8_0

Ollama
How to use g023/qwen3-tiny-v2-finetuned with Ollama:
```
ollama run hf.co/g023/qwen3-tiny-v2-finetuned:Q8_0
```

Unsloth Studio

How to use g023/qwen3-tiny-v2-finetuned 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 g023/qwen3-tiny-v2-finetuned 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 g023/qwen3-tiny-v2-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for g023/qwen3-tiny-v2-finetuned to start chatting

Docker Model Runner
How to use g023/qwen3-tiny-v2-finetuned with Docker Model Runner:
```
docker model run hf.co/g023/qwen3-tiny-v2-finetuned:Q8_0
```

Lemonade

How to use g023/qwen3-tiny-v2-finetuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull g023/qwen3-tiny-v2-finetuned:Q8_0

Run and chat with the model

lemonade run user.qwen3-tiny-v2-finetuned-Q8_0

List all available models

lemonade list

qwen3-tiny-v2-finetuned / Modelfile

g023

Upload 5 files

5a68554 verified 2 months ago

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	FROM ./Qwen3-g023-tiny-v2-FT-Q8_0.gguf
	TEMPLATE """
	{{- $lastUserIdx := -1 -}}
	{{- range $idx, $msg := .Messages -}}
	{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
	{{- end }}
	{{- 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 }}
	{{- if and $.IsThinkSet (eq $i $lastUserIdx) }}
	{{- if $.Think -}}
	{{- " "}}/think
	{{- else -}}
	{{- " "}}/no_think
	{{- end -}}
	{{- end }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}}
	<think>{{ .Thinking }}</think>
	{{ end -}}
	{{ 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
	{{ if and $.IsThinkSet (not $.Think) -}}
	<think>

	</think>

	{{ end -}}
	{{ end }}
	{{- end }}
	"""
	PARAMETER num_ctx 40000
	PARAMETER repeat_last_n 16384
	PARAMETER stop <\|im_start\|>
	PARAMETER stop <\|im_end\|>
	PARAMETER temperature 0.65
	PARAMETER top_p 0.9
	PARAMETER top_k 20
	PARAMETER min_p 0.0
	PARAMETER repeat_penalty 1.05
	PARAMETER presence_penalty 0.1
	PARAMETER frequency_penalty 0.1
	SYSTEM "You are a helpful assistant."