wesjos/Qwen3-4B-toolcall-GGUF

Eval

Before finetuning (unsloth/Qwen3-4B-unsloth-bnb-4bit)

Model	Dataset	Metric	Subset	Num	Score	Cat.0
qwen	gpqa	AveragePass@1	gpqa_extended	50	0.24	default
qwen	gpqa	AveragePass@1	gpqa_main	50	0.26	default
qwen	gpqa	AveragePass@1	gpqa_diamond	50	0.22	default
qwen	gpqa	AveragePass@1	OVERALL	150	0.24	-
qwen	gsm8k	AverageAccuracy	main	50	0.50	default
qwen	tool_bench	Act.EM	in_domain	42	0.1667	default
qwen	tool_bench	Act.EM	out_of_domain	48	0.1667	default
qwen	tool_bench	Act.EM	OVERALL	90	0.1667	-

---

After finetuning (This model)

Model	Dataset	Metric	Subset	Num	Score	Cat.0
model	gpqa	AveragePass@1	gpqa_extended	50	0.26	default
model	gpqa	AveragePass@1	gpqa_main	50	0.36	default
model	gpqa	AveragePass@1	gpqa_diamond	50	0.38	default
model	gpqa	AveragePass@1	OVERALL	150	0.3333	-
model	gsm8k	AverageAccuracy	main	50	0.68	default
model	tool_bench	Act.EM	in_domain	41	0.3171	default
model	tool_bench	Act.EM	out_of_domain	47	0.3191	default
model	tool_bench	Act.EM	OVERALL	88	0.3182	-

---

Qwen/Qwen3-4B model

Model	Dataset	Metric	Subset	Num	Score	Cat.0
model	gpqa	AveragePass@1	gpqa_extended	50	0.32	default
model	gpqa	AveragePass@1	gpqa_main	50	0.22	default
model	gpqa	AveragePass@1	gpqa_diamond	50	0.18	default
model	gpqa	AveragePass@1	OVERALL	150	0.24	-
model	gsm8k	AverageAccuracy	main	50	0.48	default
model	tool_bench	Act.EM	in_domain	43	0.1628	default
model	tool_bench	Act.EM	out_of_domain	47	0.1702	default
model	tool_bench	Act.EM	OVERALL	90	0.1667	-

---

Use this model

with llama-cli

• llama-cli -m Qwen3-4B-toolcall.Q4_K_M.gguf

with ollama

• edit a makefile named(Qwen3-4B-toolcall.Q4_K_M.txt) like:

• FROM ./Qwen3-4B-toolcall.Q4_K_M
  TEMPLATE """<|im_start|>system
  You are a helpful assistant<|im_end|>
  <|im_start|>user
  {{ .Prompt }}<|im_end|>
  <|im_start|>assistant
  """
  

• then create a model using ollama
• ollama create Qwen3-4B-toolcall.Q4_K_M -f Qwen3-4B-toolcall.Q4_K_M.txt
• then run it
• ollama run Qwen3-4B-toolcall.Q4_K_M