Instructions to use python3isfun/qwen3-1.7b-toolcall-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use python3isfun/qwen3-1.7b-toolcall-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="python3isfun/qwen3-1.7b-toolcall-gguf",
	filename="qwen3-1.7b-toolcall-Q5_K_M.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 python3isfun/qwen3-1.7b-toolcall-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_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 python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_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 python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

Use Docker

docker model run hf.co/python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

LM Studio
Jan

vLLM

How to use python3isfun/qwen3-1.7b-toolcall-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "python3isfun/qwen3-1.7b-toolcall-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "python3isfun/qwen3-1.7b-toolcall-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

Ollama
How to use python3isfun/qwen3-1.7b-toolcall-gguf with Ollama:
```
ollama run hf.co/python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
```

Unsloth Studio

How to use python3isfun/qwen3-1.7b-toolcall-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 python3isfun/qwen3-1.7b-toolcall-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 python3isfun/qwen3-1.7b-toolcall-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for python3isfun/qwen3-1.7b-toolcall-gguf to start chatting

How to use python3isfun/qwen3-1.7b-toolcall-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

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": "python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use python3isfun/qwen3-1.7b-toolcall-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

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 python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use python3isfun/qwen3-1.7b-toolcall-gguf with Docker Model Runner:
```
docker model run hf.co/python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M
```

Lemonade

How to use python3isfun/qwen3-1.7b-toolcall-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull python3isfun/qwen3-1.7b-toolcall-gguf:Q5_K_M

Run and chat with the model

lemonade run user.qwen3-1.7b-toolcall-gguf-Q5_K_M

List all available models

lemonade list

qwen3-1.7b-toolcall (Q5_K_M GGUF)

A QLoRA fine-tune of Qwen3-1.7B that reliably emits <tool_call> blocks for four local search tools, intended for on-device inference (iOS / llama.cpp). Quantized to Q5_K_M (~1.2 GB).

Base model: Qwen/Qwen3-1.7B (Apache-2.0)
Method: QLoRA (Unsloth) — r=16, α=32, all 7 attn/MLP projections, 3 epochs, lr 2e-4, cosine, AdamW-8bit
Format: bare ChatML, no <think> blocks

What it does

Given a user message, it either calls one of four tools or answers directly (math, chitchat, general knowledge, questions about the tools).

Tool	Purpose
`search_recipes(query, sort_by)`	find recipes by dish / ingredient
`search_events(query, region, max_price)`	find concerts, sports, shows
`search_food_categories(query, min_tier)`	browse dish categories by popularity tier (1–5)
`search_regions(query)`	look up which cities a region covers

A tool call is emitted as exactly:

<tool_call>
{"name": "search_recipes", "arguments": {"query": "cubano"}}
</tool_call>

Prompt format

Plain ChatML, one block per message, then an empty assistant turn to generate:

<|im_start|>system
{system prompt}<|im_end|>
<|im_start|>user
{user message}<|im_end|>
<|im_start|>assistant

System prompt the model was trained on:

You have access to these tools. To use one, reply ONLY with a tool_call block:

<tool_call>
{"name": "TOOL_NAME", "arguments": {"key": "value"}}
</tool_call>

Tools:
- search_recipes(query, sort_by): Find recipes by dish name or ingredient.
- search_events(query, region, max_price): Find concerts, sports, shows.
- search_food_categories(query, min_tier): Browse 100 dish categories by tier (1-5).
- search_regions(query): Look up which cities a region covers.

If the question does NOT need a tool, answer directly without a tool_call block.

Two-turn flow: the model emits a <tool_call>; your app runs the tool and feeds the result back as a system message (Tool results:\n{...}\n\nNow answer the user's question using the results above.), then the model writes the final natural-language answer.

Evaluation

12-test tool-use suite + a 250-example held-out set. Q5_K_M, temperature 0:

Metric	Base Qwen3-1.7B	This model (Q5_K_M)
Overall score	0.767	0.850
Pass rate (≥0.8)	8/12	10/12
Tool-call rate	70%	100%
Valid tool-call JSON	70%	100%
Correct tool name	70%	100%
Held-out tool-name acc (unseen)	47%	100%

Q5_K_M matches the full-precision model (0.85) and passes under both the trained (no-few-shot) prompt and a few-shot variant. An overfitting check showed train acc = holdout acc = 100% (zero gap) and 94% on deliberately off-template slang/typo queries — it learned the skill, not the training set.

Usage (llama.cpp)

hf download python3isfun/qwen3-1.7b-toolcall-gguf qwen3-1.7b-toolcall-Q5_K_M.gguf --local-dir .
./llama-cli -m qwen3-1.7b-toolcall-Q5_K_M.gguf --temp 0 -p "<your ChatML prompt>"

from llama_cpp import Llama
llm = Llama(model_path="qwen3-1.7b-toolcall-Q5_K_M.gguf", n_ctx=2048)
prompt = ("<|im_start|>system\n" + SYSTEM_PROMPT + "<|im_end|>\n"
          "<|im_start|>user\nFind me a recipe for tacos<|im_end|>\n"
          "<|im_start|>assistant\n")
print(llm(prompt, temperature=0.0, stop=["<|im_end|>"])["choices"][0]["text"])
# -> <tool_call>\n{"name": "search_recipes", "arguments": {"query": "tacos"}}\n</tool_call>

Notes & limitations

Trained against a specific local dataset (recipes/events/food categories/regions); tool results must come from that data for grounded final answers.
Constrained task (4 tools) — strong scores mean "no overfitting on this skill," not "flawless on every input."
A Q4_K_M variant (~1.06 GB) also exists; it matches Q5 under the trained prompt but is slightly less robust under a longer few-shot prompt.

License

Apache-2.0, inherited from the Qwen3-1.7B base model.

Downloads last month: 141

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

5-bit

Model tree for python3isfun/qwen3-1.7b-toolcall-gguf

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(277)

this model