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Model: iromu/Qwen2.5-1.5B-tools
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---
language:
- en
license: apache-2.0
base_model:
- unsloth/Qwen2.5-1.5B-Instruct
datasets:
- r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation
tags:
- qwen2.5
- qwen
- tool-calling
- function-calling
- agents
- sft
- lora
library_name: transformers
---
# Qwen2.5-1.5B-tools
Merged BF16 weights of the LoRA tool-calling fine-tune of
`unsloth/Qwen2.5-1.5B-Instruct`. Built by
`scripts/build_tools_gguf.sh Qwen2.5-1.5B-tools` (merge_and_unload of the
latest checkpoint in `checkpoints/Qwen2.5-1.5B-tools/`).
## Training
- Recipe: `TrainFinetuneRecipeForNextTokenPrediction` (NeMo AutoModel 0.5.0)
- Config: `configs/qwen25_1.5b_tools.yaml`
- LoRA: dim 32, alpha 32, dropout 0.05, target_modules `*.proj`
- Data: `sft_tools` split of `r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation`
- Learning rate: 1e-5 (cosine decay), 501 steps, global batch 64, seq length 4096
## Sibling artifacts
- GGUF quantizations: `models/Qwen2.5-1.5B-tools-GGUF/`
- NVFP4 checkpoint: `models/Qwen2.5-1.5B-tools-NVFP4/`
<!-- VALIDATION:BEGIN (auto-generated, do not edit) -->
## Validation matrix
Tool-calling validation on the `sft_tools` validation split (greedy decoding, 384 max new tokens). Throughput is single-stream greedy decode, not serving throughput.
Pretrained base (unsloth/Qwen2.5-1.5B-Instruct): 8.8% exact-args match (24/274).
Fine-tuned (BF16): 0.7% exact-args match (2/274) (-8pp vs base).
- GGUF-BF16: 2/274 (0.7%) exact, 88.6 tok/s — 100% of BF16.
- GGUF-Q4_K_M: 3/274 (1.1%) exact, 142.9 tok/s — 150% of BF16.
- GGUF-Q5_K_M: 6/274 (2.2%) exact, 131.4 tok/s — 300% of BF16.
- GGUF-Q8_0: 2/274 (0.7%) exact, 115.3 tok/s — 100% of BF16.
| Model | Quant | n | Tool call emitted | Names match | Exact args match | Δ exact vs BASE | tok/s |
|---|---|---|---|---|---|---|---|
| Qwen2.5-1.5B-tools | BASE (unsloth/Qwen2.5-1.5B-Instruct) | 274 | 272/274 (99.3%) | 59/274 (21.5%) | 24/274 (8.8%) | — | 40.6 |
| Qwen2.5-1.5B-tools | BF16 | 274 | 258/274 (94.2%) | 216/274 (78.8%) | 2/274 (0.7%) | -8pp | 38.0 |
| Qwen2.5-1.5B-tools | GGUF-BF16 | 274 | 272/274 (99.3%) | 167/274 (60.9%) | 2/274 (0.7%) | -8pp | 88.6 |
| Qwen2.5-1.5B-tools | GGUF-Q4_K_M | 274 | 248/274 (90.5%) | 49/274 (17.9%) | 3/274 (1.1%) | -7.7pp | 142.9 |
| Qwen2.5-1.5B-tools | GGUF-Q5_K_M | 274 | 260/274 (94.9%) | 79/274 (28.8%) | 6/274 (2.2%) | -6.6pp | 131.4 |
| Qwen2.5-1.5B-tools | GGUF-Q8_0 | 274 | 271/274 (98.9%) | 187/274 (68.2%) | 2/274 (0.7%) | -8pp | 115.3 |
<!-- VALIDATION:END -->

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 8960,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 12,
"num_hidden_layers": 28,
"num_key_value_heads": 2,
"pad_token_id": 151654,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.8.1",
"unsloth_fixed": true,
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"max_length": 32768,
"pad_token_id": 151654,
"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.8.1"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 32768,
"pad_token": "<|vision_pad|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}