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birgermoell/oellm-eu-tooluse-v1

oellm-eu-tooluse-v1 Function-calling / agentic post-training data, normalized to Qwen3.5's native tool-call format (<tools>…</tools> in the system turn, <tool_call>{json}</tool_call> from the assistant). Built for the OpenEuroLLM European post-training of Qwen3.5 (folded into the Qwen3.5-4B-EU "v-next" mobile model as ~10% of the SFT mix, plus a verifiable RL stage). The value here is format unification: three popular tool-use sources each encode calls differently (Hermes JSON… See the full description on the dataset page: https://huggingface.co/datasets/birgermoell/oellm-eu-tooluse-v1.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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oellm-eu-tooluse-v1

Function-calling / agentic post-training data, normalized to Qwen3.5's native tool-call format (<tools>…</tools> in the system turn, <tool_call>{json}</tool_call> from the assistant). Built for the OpenEuroLLM European post-training of Qwen3.5 (folded into the Qwen3.5-4B-EU "v-next" mobile model as ~10% of the SFT mix, plus a verifiable RL stage).

The value here is format unification: three popular tool-use sources each encode calls differently (Hermes JSON, ToolACE pythonic [fn(a=1)], Glaive marker-delimited with single-quoted argument strings). We parse each faithfully and re-render everything into the one format Qwen3.5 actually consumes at inference, so training format == inference format.

Contents

ConfigSplitRowsSchema
sft (default)train55,077{"messages": [{role, content}…], "lang": "en"}
grpotrain46,366{"prompt": [messages], "answer": "<json [{name, arguments}]>", "language": "en"}
  • —SFT rows are ready for chat-template SFT (e.g. TRL SFTTrainer) — the tool definitions are baked into the system message with Qwen3.5's exact wrapper.
  • —GRPO rows are for verifiable RL (RLVR): single-call examples with clean gold, gradeable by a structure+name+arguments reward (no reward model). One example per source conversation.

Sources & attribution

All upstream sources are Apache-2.0; this derived dataset is released under the same license.

SourceRows keptNotes
`glaiveai/glaive-function-calling-v2`50,000JSON-native backbone; includes some "don't call" (relevance) turns
`Team-ACE/ToolACE`3,184multi-turn; pythonic calls parsed via ast → JSON
`NousResearch/hermes-function-calling-v1`1,893already Hermes/Qwen-formatted

Only rows whose calls parsed cleanly into {name, arguments} are kept (consistency over volume).

Language

Currently English only — the upstream tool-use corpora are English. EU-language tool-use (distilling the same schemas/calls with non-English user turns) is planned future work.

Intended use

Teaching / evaluating function calling in the Qwen3.5 native format. Reproduction script: scripts/build_tooluse_sft.py in the OpenEuroLLM post-training repo. A self-contained, vLLM-free BFCL-style evaluator (scripts/eval_tool_calling.py) grades name/args/format accuracy plus refusal/relevance on a held-out Glaive slice.

Limitations

English-only; single-call gold for the GRPO split (parallel/multi-call left to SFT); ToolACE coverage is a clean subset, not the full corpus. Inherits any artifacts of the upstream sources.