datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive-function-calling-v2glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
reasoning-v1-20m
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m.glaive-function-callingThis dataset consists of 52k samples generated through Glaive for the task of function calling, in the following format-
SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
JSON function definiton
}
USER: user message
ASSISTANT: assistant message
Function call invocations are formatted as-
ASSISTANT: <functioncall> {json function call}
Response to the function call is formatted as-
FUNCTION RESPONSE: {json… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/glaive-function-calling.glaive-function-calling-v2-sharegptThe glaive-function-calling-v2 dataset in sharegpt format.
You can use it in LLaMA Factory by specifying --dataset glaive_toolcall_100k.
glaive-function-calling-v2Modified version of the glaiveai/glaive-function-calling-v2 dataset
All samples in the glaive dataset is converted into the following format for better interoperability
[
{
"role":"system",
"content":"You are a helpful assistant with access to the functions.",
"functions":[
{
"name":"generate_password",
"description":"Generate a random password with specified criteria",
"parameters":{… See the full description on the dataset page: https://huggingface.co/datasets/Dulsara/glaive-function-calling-v2.glaive-function-calling-v2-openai-native
glaive-function-calling-v2-openai-native
glaiveai/glaive-function-calling-v2 restructured into the native OpenAI / TRL
format: tools is a typed column and tool_calls[].function.arguments is a
real object — not JSON inside a string.
The original is widely used (69k downloads/month) but inactive for ~3 years, and
ships tool calls as <functioncall> text blobs with Python-quoted arguments.
Existing repackagings either keep ShareGPT with tools as a string, or carry
no license at all.… See the full description on the dataset page: https://huggingface.co/datasets/Archangel-system/glaive-function-calling-v2-openai-native.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
noteflow-research-pilots
Keep the failed attempts. Check the artifact.
Versioned public development evidence from Robot Reel × Skills Anywhere × EvalArc, recorded 14 September 2026 on an NVIDIA L40S, with separate scripted Harbor controls on CPU and separate GPU context-control and agent-requested MCP handoff cohorts recorded 19 September 2026. This is an inspectable engineering casebook, not a held-out benchmark or training corpus with established efficacy.
Configuration
Actual experiment
What… See the full description on the dataset page: https://huggingface.co/datasets/glayguo/noteflow-research-pilots.glaive-function-calling-v2-llama
Glaive's Function Calling V2 for Llama2
Glaive's Function Calling V2 dataset, formatted according to the Llama2 chat schema, with all the data that I wasn't able to automatically convert removed manually.
Adds a special <function> token. Here's an example prompt:
<s>[INST] <<SYS>>
<function>Available functions:
<function>{
"name": "generate_password",
"description": "Generate a random password with specified criteria",
"parameters": {
"type": "object"… See the full description on the dataset page: https://huggingface.co/datasets/rizerphe/glaive-function-calling-v2-llama.glassballai
GlassBallAI: A Dataset of LLM Market Predictions (Made with Google Gemini Models)
The dataset contains thousands of stock market predictions generated across multiple Google Gemini models.This dataset contains live-captured inference states that cannot be reproduced due to model updates and information leakage.
Gemini 2.5 Pro Example Evaluations:
Blue lines represent 10-day predictions, while the red line represents the actual trend
Discussion: Hugging Face Forum Website:… See the full description on the dataset page: https://huggingface.co/datasets/louidev/glassballai.pine-of-glass-sessions
Coding agent session traces for thomasmustier/pine-of-glass-sessions
This dataset contains redacted coding agent session traces collected while working on tmustier/pine-of-glass. The traces were exported with pi-share-hf from a local pi workspace. The traces were filtered to keep only sessions that passed deterministic redaction and LLM review.
Data description
Each *.jsonl file is a redacted pi session. Sessions are stored as JSON Lines files where each line is a… See the full description on the dataset page: https://huggingface.co/datasets/thomasmustier/pine-of-glass-sessions.glaive_toolcall_zhBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Translated by GPT-3.5.
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.
fin-glaive
Fin-Glaive: 645K Financial Instruction and Reasoning Examples
Fin-Glaive is a large-scale English dataset for financial instruction tuning, financial question answering, and reasoning-focused language-model post-training. It contains 645,232 question–reasoning–answer examples mined from Glaive Reasoning v1 20M.
The dataset and its role in the post-training pipeline are described in Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning.… See the full description on the dataset page: https://huggingface.co/datasets/whoisjiji/fin-glaive.glaive-function-calling-v2-zephyr
Glaive's Function Calling V2 for Zephyr-7B-alpha
Glaive's Function Calling V2 dataset, formatted according to the chat schema zephyr uses, with all the data that I wasn't able to automatically convert removed.
Adds three new roles: definition, function and call. Here's an example prompt:
<|definition|>
{
"name": "generate_password",
"description": "Generate a random password with specified criteria",
"parameters": {
"type": "object",
"properties": {… See the full description on the dataset page: https://huggingface.co/datasets/rizerphe/glaive-function-calling-v2-zephyr.glaive-function-calling-v2-ru-parsed
Glaive Function Calling V2 Parsed (RU)
This dataset is a fork of an original mizinovmv/glaive-function-calling-v2-ru.
Structure
train (102860 rows)
messages
functions
test (9681 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-ru-parsed.startup-interviewsru_glaive-function-calling-v2
Glaive Function Calling
https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Mille-Pensees-Dataset
Mille-Pensées-Dataset
Dataset Summary
The Mille-Pensées-Dataset is a math reasoning dataset with a 50% french / 50% english composition used to train the Mille-Pensées french reasoning model.
The source data comes from the following english math reasoning datasets:
s1K-1.1
OpenThoughts3-1.2M
OpenR1-Math-220k
OpenMathReasoning
Nemotron-Post-Training-Dataset-v1
LIMO-v2
DeepMath-103K
AM-DeepSeek-R1-0528-Distilled
The french reasoning data was obtained by translating the… See the full description on the dataset page: https://huggingface.co/datasets/GLauzza/Mille-Pensees-Dataset.glaive-function-calling-v2-parsed
Glaive Function Calling V2 Parsed
This dataset is a fork of an original - glaiveai/glaive-function-calling-v2.
Structure
train (100199 rows)
messages
functions
test (12553 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat (their… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-parsed.shlyokavitsa-pairs
Shlyokavitsa → Cyrillic restoration pairs
210,236 (Latin, Cyrillic) phrase pairs for restoring shlyokavitsa (Bulgarian typed on a
Latin keyboard) back into Cyrillic. Built from Bulgarian Wikipedia, so it can be shared
under the same licence as its source.
{"latin": "sreshta se na dalbochina okolo", "cyrillic": "среща се на дълбочина около", "n_words": 5, "page_id": 1041}
Filed under translation because that is the closest category the Hub offers, but the task is
script… See the full description on the dataset page: https://huggingface.co/datasets/glassbox/shlyokavitsa-pairs.evalarc-independent-swe
Independent-source SWE workflow records
36 Qwen3-8B attempts compare four fixed workflows on three public SWE-bench
Verified tasks. There are no accepted attempts: 31 have assessable native
reports and five retain an upstream infrastructure flag, so their task outcome
is uncertain. Eight attempts produced nonempty patches. One generation request
has incomplete usage.
Inspect the interactive report
· English method · 中文方法
· Offline review and exact raw records
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/glayguo/evalarc-independent-swe.SFT_glaive_toolcall_en
Preparing Your Dataset
Once you’ve decided that fine-tuning is the best approach—after optimizing your prompt as much as possible and identifying remaining model issues—you’ll need to prepare training data. Start by creating a diverse set of example conversations that mirror those the model will handle during production.
Each example should follow this structure below, consisting of a list of messages. Each message must include a role, content, and an optional name. Make sure some… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/SFT_glaive_toolcall_en.restructured-glaive-function-calling-v2
Glaive Function Calling V2 (Structured)
This dataset is a cleaned and structured version of the originalGlaive Function Calling V2.
The goal of this dataset is to make the conversations easier to use for training tool-calling / function-calling language models, such as:
Llama
Qwen
Mistral
DeepSeek
other OpenAI-compatible tool calling models
The original dataset stores conversations as raw text.This version converts them into a structured message format suitable for modern LLM… See the full description on the dataset page: https://huggingface.co/datasets/muhammadravi251001/restructured-glaive-function-calling-v2.kakugo-gla
Kakugo Scottish Gaelic dataset
[Paper] [Code] [Model]
A synthetically generated conversation dataset for training in Scottish Gaelic.
This dataset contains synthetic conversational data and translated instructions designed to train Small Language Models (SLMs) for Scottish Gaelic. It was generated using the Kakugo pipeline, a method for distilling high-quality capabilities from a large teacher model into low-resource language models. The teacher model used to generate this… See the full description on the dataset page: https://huggingface.co/datasets/ptrdvn/kakugo-gla.glaive-function-calling-v2glaive-function-calling-v2-pl
Dataset Card for glaive-function-calling-v2-pl Dataset
Dataset Description
This is a fragment of glaiveai/glaive-function-calling-v2 dataset translated to polish.
It contains first 5k instructions of the original dataset. Only instructions having function definitions or function calls are included, instructions without functions (ordinary unstructured) from the original dataset are skipped.
Some repeating instructions were intentionally modified to increase diversity of… See the full description on the dataset page: https://huggingface.co/datasets/mpieck/glaive-function-calling-v2-pl.glaive-code-assistant
Glaive Code Assistant
Glaive Code Assistant dataset formatted for training assistant models with the following prompt template:
<s>[INST] {question} [/INST] {answer} </s>
Trained model can be prompted in Llama style:
<s>[INST] {{ user_msg }} [/INST]
glaive-tool-use-reasoning-ar
Arabic Glaive Tool-Use Reasoning
Arabic translation and augmentation of the Glaive Function Calling data for research on Arabic function calling, tool selection, argument generation, and tool-call verification.
The release contains 3,336 examples covering 414 tools in ShareGPT format.
Dataset Structure
Each example contains:
{
"tools": [...],
"conversations": [...]
}
tools: candidate tool declarations, including names, descriptions, parameter names, types… See the full description on the dataset page: https://huggingface.co/datasets/Makeen-AraFC/glaive-tool-use-reasoning-ar.glaiveai_glaive-code-assistant-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
glaiveai_glaive-code-assistant-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
glaiveai/glaive-code-assistant with responses regenerated with gemini-2.0-flash-thinking-exp-1219.
Generation Details
If BlockedPromptException, StopCandidateException, or InvalidArgument was returned, the sample was skipped.
If ["candidates"][0]["safety_ratings"] == "SAFETY" the sample was skipped.
If ["candidates"][0]["finish_reason"] != 1 the sample was skipped.
model =… See the full description on the dataset page: https://huggingface.co/datasets/PJMixers-Dev/glaiveai_glaive-code-assistant-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT.