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Project-Solace-1.0-GLM5.2-Fable5-Opus5-DeepseekV4-Pro0813-Preview

Name: Project-Solace-1.0-GLM5.2-Fable5-Opus5-DeepseekV4-Pro0813-Preview
Creator: SolsticeAI
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Project Solace

Built From the Best.

25B+ tokens · 64 datasets · Frontier-model distillation · One unified corpus

Project Solace is a large-scale, high-signal training corpus built from frontier-model outputs and downstream distillations.

It brings together 64 carefully selected datasets, spanning coding, reasoning, mathematics, agentic trajectories, conversations, and software engineering, and unifies them into a single training-ready corpus.

25B+ tokens. 15M+ examples. 64 sources. One corpus.

The Corpus

Training data is easy to make large.

Making it useful at scale is harder.

Project Solace focuses on frontier-model distillation at training scale: aggregating high-quality outputs from contemporary frontier systems and carefully selected downstream distillations, then processing them into a consistent, deduplicated format.

The result is a corpus designed for researchers and builders who need both scale and signal.

Built From the Best.

At a Glance


Scale	25B+ estimated tokens
Examples	15M+
Sources	64 datasets
Source groups	7 major groups
Date range	May–August 2026
Raw source data	~115 GB
Output	Single JSONL corpus
Deduplication	Exact SHA256 deduplication
Schema	Messages + source + model

64 Source Datasets

Project Solace would not exist without the researchers and creators who released the underlying datasets.

Every source is preserved in the corpus manifest, with attribution to its originating repository.

GLM-5.2

#	Repository
1	`OnepointfiveHz/glm-5.2-openhands-rollout-0806`
2	`mgoin/open-perfectblend-glm5.2-regen`
3	`OnepointfiveHz/glm-5.2-openhands-rollout-0805`
4	`OnepointfiveHz/glm-5.2-openhands-rollout-0801_07`
5	`JessieWei/GLM-5.2-FP8-nemotron-codealpaca-thinking`
6	`JessieWei/GLM-5.2-FP8-nemotron-codealpaca`
7	`mgoin/GLM-5.2-FP8-magpie-ultrachat`
8	`DavidrPatton/Fable-5-GLM-5.2-Traces`
9	`ansulev/GLM-5.2-Conversation`
10	`AgentNativeResearchLab/arc-agi3-cc-glm5.2-ls20`
11	`AgentNativeResearchLab/arc-agi3-cc-glm5.2-su15`
12	`AletheiaResearch/GLM-5.2-Agent`
13	`greghavens/glm-5.2-coding-and-debugging-traces`
14	`marin-community/openthoughts4-code-9168-prompts-glm-5.2-n4`
15	`AgentNativeResearchLab/fm-open-problems-glm5.2-trajectories`
16	`AgentNativeResearchLab/lhtb-glm5.2-trajectories`
17	`turintech/GLM-5.2-MaxMin108-saliency`
18	`JacobChang/GLM5.2-B200-profiles`

Fable 5

#	Repository
19	`usernamebetter/fable5-traces-agentic`
20	`usernamebetter/fable5-traces-agentic-clean`
21	`usernamebetter/fable5-traces-agentic-clean-v2`
22	`saidutta69/fable-5-premium`
23	`Manusagents/Vibe-Coding-Claude-Fable-5`
24	`Biomechanist/fable-5-coding-and-debugging-traces-harmonized`
25	`AgentNativeResearchLab/arc-agi3-cc-fable5-ls20`
26	`AgentNativeResearchLab/arc-agi3-cc-fable5-g50t`
27	`Solstice-AI/Complete-FABLE.5-traces-2M`
28	`aisamdasu/algocean-fable5-traces`
29	`Nexlab/fable5-agentic-coding-sft`
30	`Swarm-AI-Research/fable5-traces-sft`

GPT-5.6 Sol

#	Repository
31	`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-g50t`
32	`greghavens/gpt-5.6-sol-coding-and-debugging-traces`
33	`Biomechanist/gpt-5.6-sol-coding-and-debugging-traces-harmonized`
34	`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-r11l`
35	`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-ls20`
36	`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-su15`
37	`AgentNativeResearchLab/fm-open-problems-gpt5.6-sol-trajectories`

GPT-5.5

#	Repository
38	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-g50t`
39	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-s5i5`
40	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-r11l`
41	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-ls20`
42	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-ar25`
43	`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-su15`
44	`AletheiaResearch/GPT-5.5-Codex`

DeepSeek V4 Pro

#	Repository
45	`sequelbox/Mitakihara2-DeepSeek-V4-Pro`
46	`ansulev/deepseek-v4-pro-tachibana4`
47	`sequelbox/Titanium4-DeepSeek-V4-Pro`
48	`trjxter/DeepSeek-V4-Pro-Reasoning-8000x`
49	`ansulev/deepseek-v4-pro-agent`
50	`Jackrong/DeepSeek-V4-Pro-Distilled-200K`
51	`Cartinoe5930/ResearchMath-14k_deepseek-v4-pro`
52	`r0b0tlab/deepseek-v4-pro-0813-agentic`
53	`fxiao0369/deepseek-v4-pro-swebench-replay`

Qwen 3.8

#	Repository
54	`CodeFlame/Qwen3.8-GLM5.2-Kimi-K3-GPT5.6-Gemini-3.1-Claude-Fable5-Mythos5-distillation`
55	`RadixArk/Qwen3.8-27B-Regen-Mixture-v1`
56	`r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation`
57	`guell00/qwen-3.8-code`

Kimi K3 / Opus 4.7 / Multi-Model

#	Repository
58	`lzy510016411/fable5-gpt5.5-opus4.7-mixed-agent-traces`
59	`Hein1212/claude-opus-4.6-4.7-reasoning-8.7k`
60	`Manusagents/Mythos-5-and-Fabel-5-Class-Model-Outputs`
61	`AgentNativeResearchLab/fm-open-problems-kimi-k3-trajectories`
62	`thientrangngv/SERA-KimiK3-Django-SWEAgent-Raw-T1`
63	`thientrangngv/SERA-KimiK3-Django-SWEAgent-Cliff32k-T1`
64	`Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset`

64 sources. One corpus.

What Makes Solace Different

The open training-data ecosystem has no shortage of synthetic data.

The problem is not that data is synthetic.

The problem is signal.

Low-quality generations, repetitive instruction tuning, legacy-model outputs, and recycled datasets can add enormous token counts without adding equivalent capability.

Solace takes a different approach.

It centers the corpus around contemporary frontier-model outputs and high-quality downstream distillations, with source-level verification, measurement, normalization, and exact deduplication.

Frontier signal

The corpus spans major contemporary reasoning, coding, agentic, and general-purpose systems.

Training scale

More than 25B estimated tokens across 15M+ examples.

Source transparency

Every example carries its originating dataset and model metadata.

Exact deduplication

Identical normalized examples are removed across the merged corpus using SHA256 hashing.

Unified format

Everything becomes one consistent JSONL structure.

The Numbers

Metric	Value
Total examples	~15M+
Estimated tokens	25B+
Datasets merged	64
Source groups	7
Date range	May–August 2026
Raw source data	~115 GB
Final output	Single JSONL
Deduplication	Exact SHA256
Schema	Messages + source + model

Token counts are estimates derived from measurements of the underlying source data and should be treated accordingly.

What's Inside

Solace covers several distinct capability domains.

Source group	Datasets	Primary signal
GLM-5.2	18	Agent rollouts, coding, reasoning, conversations
Fable 5	12	Agentic coding, multi-turn traces, SFT
GPT-5.6 Sol	7	Coding, debugging, ARC-style reasoning
GPT-5.5	7	Code generation, ARC-style reasoning, agents
DeepSeek V4 Pro	9	Mathematics, STEM, SWE, reasoning
Qwen 3.8	4	Multi-teacher distillation, coding
Kimi / Opus / Multi	7	Reasoning, SWE agents, mixed-model distillation

The original source manifest remains the authoritative record of individual datasets and provenance.

Data Format

Every example is represented as a single JSON object:

{
  "messages": [
    {
      "role": "system",
      "content": "..."
    },
    {
      "role": "user",
      "content": "..."
    },
    {
      "role": "assistant",
      "content": "..."
    }
  ],
  "source": "repo/dataset-name",
  "model": "glm-5.2"
}

The source and model fields make it possible to filter, analyze, rebalance, or selectively train on specific portions of the corpus.

Compatibility

Solace uses a conventional conversational messages schema and is designed to work with common SFT tooling.

Framework / Tool	Compatibility
Hugging Face TRL / SFTTrainer	✅
Axolotl	✅
LLaMA-Factory	✅
torchtune	✅
Standard JSONL tooling	✅
OpenAI-style messages pipelines	✅

Compatibility may depend on the exact training configuration and tokenizer used by the downstream system.

Datasets Included

GLM-5.2 — 18 datasets

Dataset	Size	Type
`OnepointfiveHz/glm-5.2-openhands-rollout-0806`	13.75 GB	Agent rollouts
`mgoin/open-perfectblend-glm5.2-regen`	14.60 GB	On-policy regeneration
`OnepointfiveHz/glm-5.2-openhands-rollout-0805`	3.81 GB	Agent rollouts
`OnepointfiveHz/glm-5.2-openhands-rollout-0801_07`	1.94 GB	Agent rollouts
`JessieWei/GLM-5.2-FP8-nemotron-codealpaca-thinking`	12.90 GB	Thinking traces
`JessieWei/GLM-5.2-FP8-nemotron-codealpaca`	5.65 GB	Coding
`mgoin/GLM-5.2-FP8-magpie-ultrachat`	2.54 GB	Reasoning
`DavidrPatton/Fable-5-GLM-5.2-Traces`	1.45 GB	Dual teacher
`ansulev/GLM-5.2-Conversation`	0.45 GB	Conversation
`AgentNativeResearchLab/arc-agi3-cc-glm5.2-ls20`	0.05 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-cc-glm5.2-su15`	0.11 GB	ARC traces
`AletheiaResearch/GLM-5.2-Agent`	0.11 GB	Agent
`greghavens/glm-5.2-coding-and-debugging-traces`	0.03 GB	Coding
`marin-community/openthoughts4-code-9168-prompts-glm-5.2-n4`	4.13 GB	Code reasoning
`AgentNativeResearchLab/fm-open-problems-glm5.2-trajectories`	0.11 GB	Trajectories
`AgentNativeResearchLab/lhtb-glm5.2-trajectories`	0.10 GB	Trajectories
`turintech/GLM-5.2-MaxMin108-saliency`	0.69 GB	Saliency
`JacobChang/GLM5.2-B200-profiles`	0.62 GB	Profiles

Fable 5 — 12 datasets

Dataset	Size	Type
`usernamebetter/fable5-traces-agentic`	1.36 GB	Agentic
`usernamebetter/fable5-traces-agentic-clean`	1.83 GB	Agentic
`usernamebetter/fable5-traces-agentic-clean-v2`	0.69 GB	Agentic v2
`saidutta69/fable-5-premium`	2.18 GB	Premium
`Manusagents/Vibe-Coding-Claude-Fable-5`	0.43 GB	Vibe coding
`Biomechanist/fable-5-coding-and-debugging-traces-harmonized`	0.58 GB	Harmonized
`AgentNativeResearchLab/arc-agi3-cc-fable5-ls20`	0.47 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-cc-fable5-g50t`	0.15 GB	ARC traces
`Solstice-AI/Complete-FABLE.5-traces-2M`	1.94 GB	Complete traces
`aisamdasu/algocean-fable5-traces`	0.73 GB	Coding
`Nexlab/fable5-agentic-coding-sft`	0.55 GB	Agentic SFT
`Swarm-AI-Research/fable5-traces-sft`	0.05 GB	SFT

GPT-5.6 Sol — 7 datasets

Dataset	Size	Type
`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-g50t`	1.27 GB	ARC reasoning
`greghavens/gpt-5.6-sol-coding-and-debugging-traces`	0.98 GB	Coding
`Biomechanist/gpt-5.6-sol-coding-and-debugging-traces-harmonized`	0.95 GB	Harmonized
`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-r11l`	0.55 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-ls20`	0.29 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.6sol-su15`	0.24 GB	ARC traces
`AgentNativeResearchLab/fm-open-problems-gpt5.6-sol-trajectories`	0.80 GB	Trajectories

GPT-5.5 — 7 datasets

Dataset	Size	Type
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-g50t`	0.62 GB	ARC reasoning
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-s5i5`	0.59 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-r11l`	0.35 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-ls20`	0.14 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-ar25`	0.11 GB	ARC traces
`AgentNativeResearchLab/arc-agi3-codex-gpt5.5-su15`	0.40 GB	ARC traces
`AletheiaResearch/GPT-5.5-Codex`	0.68 GB	Agent traces

DeepSeek V4 Pro — 9 datasets

Dataset	Size	Type
`sequelbox/Mitakihara2-DeepSeek-V4-Pro`	0.91 GB	Coding
`ansulev/deepseek-v4-pro-tachibana4`	0.85 GB	Coding
`sequelbox/Titanium4-DeepSeek-V4-Pro`	0.84 GB	Coding
`trjxter/DeepSeek-V4-Pro-Reasoning-8000x`	0.51 GB	Reasoning
`ansulev/deepseek-v4-pro-agent`	0.26 GB	Agent
`Jackrong/DeepSeek-V4-Pro-Distilled-200K`	7.42 GB	Math / STEM
`Cartinoe5930/ResearchMath-14k_deepseek-v4-pro`	2.50 GB	Math
`r0b0tlab/deepseek-v4-pro-0813-agentic`	0.50 GB	Agentic
`fxiao0369/deepseek-v4-pro-swebench-replay`	0.37 GB	SWE-bench

Qwen 3.8 — 4 datasets

Dataset	Size	Type
`CodeFlame/Qwen3.8-GLM5.2-Kimi-K3-GPT5.6-Gemini-3.1-Claude-Fable5-Mythos5-distillation`	1.35 GB	Multi-teacher
`RadixArk/Qwen3.8-27B-Regen-Mixture-v1`	5.95 GB	Regeneration mixture
`r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation`	0.77 GB	Multi-teacher
`guell00/qwen-3.8-code`	0.11 GB	Code

Kimi K3 / Opus 4.7 / Multi-Model — 7 datasets

Dataset	Size	Type
`lzy510016411/fable5-gpt5.5-opus4.7-mixed-agent-traces`	0.23 GB	Multi-model
`Hein1212/claude-opus-4.6-4.7-reasoning-8.7k`	0.24 GB	Reasoning
`Manusagents/Mythos-5-and-Fabel-5-Class-Model-Outputs`	2.72 GB	Multi-model
`AgentNativeResearchLab/fm-open-problems-kimi-k3-trajectories`	0.05 GB	Trajectories
`thientrangngv/SERA-KimiK3-Django-SWEAgent-Raw-T1`	0.08 GB	SWE agent
`thientrangngv/SERA-KimiK3-Django-SWEAgent-Cliff32k-T1`	0.05 GB	SWE agent
`Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset`	4.20 GB	Multi-teacher

Deduplication

Three source-level duplicates were identified and excluded:

Removed	Duplicate of	Reason
`sequelbox/Tachibana4-DeepSeek-V4-Pro`	`ansulev/deepseek-v4-pro-tachibana4`	Same file
`TeichAI/DeepSeek-v4-Pro-Agent`	`ansulev/deepseek-v4-pro-agent`	Mirror upload
`ufrik/qwen3.8-max-glm5.2-distillation-51389`	`r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation`	Subset

After source-level filtering, the final merged corpus also undergoes exact example-level deduplication.

Each normalized messages object is hashed using SHA256.

Only identical examples are removed.

No fuzzy matching is used.

If two examples differ, both remain.

How Solace Was Built

Project Solace was assembled as a systematic data-engineering pipeline rather than a simple dataset concatenation.

01 — Discovery

Hundreds of searches across Hugging Face were used to identify candidate datasets covering frontier-model outputs, reasoning traces, coding trajectories, agentic tasks, and distillation.

More than 70 candidate datasets were initially identified.

02 — Verification

Each candidate was inspected for:

Source provenance
Model attribution
Dataset structure
Data quality
Relevance to reasoning, coding, or agentic training
Available file formats
Licensing and source metadata

Datasets that did not meet the project's criteria were excluded.

03 — Measurement

Dataset metadata and actual source files were inspected to estimate storage requirements, row counts, text volume, and token counts.

Where possible, measurements were taken from actual data rather than relying solely on repository-level metadata.

04 — Normalization

The source datasets use a variety of formats, including:

JSONL
JSON
CSV
Parquet
GZIP
Zstandard
TAR
TAR.GZ
TAR.ZST
ZIP

These are normalized into a common conversational schema.

05 — Deduplication

The normalized messages are hashed using SHA256.

Exact duplicates across the merged corpus are removed.

06 — Distribution

The processed data is consolidated into a single JSONL corpus with source and model metadata preserved alongside every example.

Archive Handling

Some upstream datasets are distributed as extremely large nested archives.

For example, the first source,

OnepointfiveHz/glm-5.2-openhands-rollout-0806

is distributed as a .tar.zst archive measuring approximately 14.8 GB compressed, with roughly 80 GB uncompressed and approximately 291K files.

The archive contains nested .tgz files.

The processing pipeline therefore streams archive members instead of extracting the entire archive to disk.

The general strategy is:

Decompress the Zstandard stream.
Iterate through TAR members.
Process relevant data files directly.
Recursively handle nested archives.
Release temporary files immediately.
Preserve only the normalized output.

This avoids requiring the full uncompressed archive footprint on local storage.

Usage

Start Training

from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

dataset = load_dataset(
    "Solstice-AI/Project-Solace",
    data_files="solace_final.jsonl",
    split="train"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    args=SFTConfig(output_dir="./output")
)

trainer.train()

The dataset is already normalized into a conversational format, so downstream pipelines can operate directly on the messages field.

Load and Explore

import json

with open("solace_final.jsonl") as f:
    for line in f:
        example = json.loads(line)

        print(
            f"Model: {example['model']}, "
            f"Source: {example['source']}"
        )

        print(
            f"Messages: "
            f"{len(example['messages'])} turns"
        )

Filter by Model

import json

with open("solace_final.jsonl") as f:
    examples = [json.loads(line) for line in f]

glm_data = [
    x for x in examples
    if x["model"] == "glm-5.2"
]

fable_data = [
    x for x in examples
    if x["model"] == "fable5"
]

gpt_data = [
    x for x in examples
    if x["model"] == "gpt56sol"
]

deepseek_data = [
    x for x in examples
    if x["model"] == "deepseek-v4"
]

Filter by Capability

Because source metadata is preserved, researchers can construct targeted subsets for specific experiments.

import json

with open("solace_final.jsonl") as f:
    examples = [json.loads(line) for line in f]

coding = [
    x for x in examples
    if any(
        keyword in json.dumps(x).lower()
        for keyword in [
            "code",
            "debug",
            "programming",
            "swe"
        ]
    )
]

reasoning = [
    x for x in examples
    if any(
        keyword in json.dumps(x).lower()
        for keyword in [
            "reason",
            "proof",
            "math",
            "logic"
        ]
    )
]

Manifest

The accompanying manifest.json records per-dataset metadata, including:

Source repository
Model family
Dataset size
Row counts
Character counts
Token estimates
Processing statistics
Download information
Source-level provenance

The manifest is the authoritative reference for dataset-level metadata.

Who Is This For?

Fine-tuners

Build specialized models using large-scale frontier-model distillation without assembling dozens of independent sources yourself.

Researchers

Study reasoning, coding, agent behavior, model distillation, synthetic-data scaling, and cross-model training.

Builders

Use a broad training corpus as a foundation for experiments, prototypes, and specialized models.

The open-source community

Access a consolidated corpus while retaining source attribution and provenance.

Why Solace?

Built From the Best.

Solace is built around a straightforward premise:

The quality of a training corpus is determined by the quality of the signal inside it.

A dataset can contain billions of tokens and still teach very little.

Solace instead combines:

Frontier-model outputs
High-quality downstream distillations
Agentic trajectories
Coding traces
Reasoning data
Mathematical and STEM data
Multi-teacher datasets
Source-level attribution
Exact deduplication
A unified training format

The objective is not simply to make the corpus larger.

It is to make every additional source count.

What Makes This Different

Traditional Dataset	Project Solace
Mixed-quality synthetic data	Frontier-model outputs and selected distillations
Legacy + contemporary sources	Strong emphasis on contemporary 2026 sources
Unknown provenance	Source and model metadata preserved
Unmeasured token counts	Measurements based on source data
Multiple formats	One normalized JSONL corpus
Duplicate-heavy aggregation	Exact SHA256 deduplication
Generic instruction data	Coding, reasoning, agentic, STEM, and SWE traces
Small-scale collections	15M+ examples / 25B+ tokens
Source attribution often lost	Dataset-level provenance retained

Acknowledgments

Project Solace stands on the work of the open-source dataset community.

The corpus aggregates 64 upstream datasets created and published by researchers and independent developers across the ecosystem.

Our thanks go to:

AgentNativeResearchLab, AletheiaResearch, Biomechanist, Cartinoe5930, CodeFlame, DavidrPatton, Hein1212, Jackrong, JacobChang, JessieWei, Manusagents, Nexlab, OnepointfiveHz, RadixArk, Solstice-AI, Swarm-AI-Research, TeichAI, UrbanOcarina, ansulev, fxiao0369, greghavens, guell00, lzy510016411, mgoin, r0b0tlab, saidutta69, sequelbox, thientrangngv, trjxter, turintech, usernamebetter, and the many other contributors represented in the source manifest.

Every upstream dataset retains its own attribution, licensing, and terms.

Please consult the individual source repositories before redistributing derived data.

License & Provenance

Project Solace is an aggregation of independently published datasets.

Individual source datasets retain their original licenses and terms.

The merged corpus is provided as-is for research and development purposes.

Users are responsible for reviewing the license and usage requirements of each upstream dataset before using or redistributing the corresponding data.

The complete source mapping is preserved in manifest.json.

Citation

@dataset{solace2026,
  title={Project Solace: Frontier Model Distillation Corpus},
  author={Solstice-AI},
  year={2026},
  note={64 datasets, 25B+ tokens, exact-deduplicated}
}

When using Project Solace in research, please cite both the corpus and the relevant upstream datasets that materially contributed to your work.

Project Solace

Built From the Best.

25B+ tokens. 15M+ examples. 64 datasets. One corpus.

Solstice-AI

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