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dphn/Dolphin-2.9.1-Phi-3-Kensho-4.5B-abliterated-v3

dphn Phi 6.5B
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Response includes
  • classification m1
  • files 8
  • benchmarks 5 entries
  • hub_downloads_all_time 923
  • author_summary 5 models
  • readme_text full
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
923
30 last 30d - cooling
Likes
10
Model age
2.4y ago
created 2024-05-20

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

Downloads over time
Now934→from481↑94%
458632806979481 on Jul 16, 2025934 on Oct 12Jul '25Sep '25Nov '25JanMarMayJulSep
Jul 16, 2025 → Oct 12 · 105 snapshots · spans 453 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Arena-Battles 21097 LM-Arena
LM Arena Elo 1082.1966432448253 LM-Arena
Arena-Elo-Lower 1076.191750651914 LM-Arena
Arena-Elo-Upper 1088.2015358377366 LM-Arena
Arena-Rank 200 LM-Arena

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
mit
Languages
en
Tags
transformers safetensors mistral text-generation en dataset:cognitivecomputations/Dolphin-2.9 dataset:teknium/OpenHermes-2.5 dataset:m-a-p/CodeFeedback-Filtered-Instruction dataset:cognitivecomputations/dolphin-coder dataset:cognitivecomputations/samantha-data dataset:microsoft/orca-math-word-problems-200k dataset:Locutusque/function-calling-chatml

Related

Total size
12.2 GB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2024-05-22 17:43

Files by quantization

Auxiliary files 8 files 12.2 GB
model-00001-of-00003.safetensors 4.65 GB e43eb0a2 download
model-00002-of-00003.safetensors 4.64 GB 177a4195 download
model-00003-of-00003.safetensors 2.89 GB 646e498a download
model.safetensors.index.json 40.8 KB 1b852614 download
README.md 3.38 KB e2c30def download
.gitattributes 1.48 KB a6344aac download
config.json 678 B 2c4e2fe6 download
generation_config.json 161 B 410737ba download

README current version from Hugging Face


license: mit
language:

  • en
    base_model:
  • unsloth/Phi-3-mini-4k-instruct
    datasets:
  • cognitivecomputations/Dolphin-2.9
  • teknium/OpenHermes-2.5
  • m-a-p/CodeFeedback-Filtered-Instruction
  • cognitivecomputations/dolphin-coder
  • cognitivecomputations/samantha-data
  • microsoft/orca-math-word-problems-200k
  • Locutusque/function-calling-chatml
  • internlm/Agent-FLAN

Dolphin 2.9.1 Phi-3 Kensho 4.5b 🐬 (Abliterated)

Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations.
Uncensored by FailSpy

Discord
Discord: https://discord.gg/cognitivecomputations

Our appreciation for the sponsors of Dolphin 2.9:

This model utilizes PEFT layer replication at inference time to duplicate layers and increase parameter count. This works with both the merged model that comes stock with this repository,
and the adapter that is attached as well. Performance will be similar with both methods, but VRAM use is considerably less when using the adapter.
This model was initialized using Unsloth's Mistralfied Phi-3-Instruct-4k. If you choose to use the adapter method, please attach it to their model.

This model is based on Phi-3-Mini-Instruct-4k, and is governed by the MIT license in which Microsoft released Phi-3.

The base model has 4k context, and the qLoRA fine-tuning was with 4k sequence length.

The model's weights were then adjusted to ablate and inhibit refusals based on the methodology described in Refusal in LLMs is mediated by a single direction'
This effectively uncensors the model whilst minimizing affecting other features in the model.

It took 2.5 days on 8xL40S node provided by Crusoe Cloud

This model uses ChatML prompt template format.

example:

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.

Dolphin is licensed according to the MIT license. I grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.

Built with Axolotl

README history 4 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2024-05-22Update README.md7639c663.4 KB
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  2. 2024-05-22Update README.md0b3b85b3.3 KB
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  3. 2024-05-22Update README.md12c44583.3 KB
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  4. 2024-05-20Upload MistralForCausalLMc0e9b7a5.1 KB
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Discussions 2 threads

  1. 2024-05-26phi-3 small has been released and it is a game changerclosed2 💬#2
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  2. 2024-05-26Tokenizer files missingopen2 💬#1
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