Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…
Open-weight model · Text generation
Qwen3-4B-Instruct-2507-abliterated
by Seva glivorem/Qwen3-4B-Instruct-2507-abliterated
Qwen3-4B-Instruct-2507-abliterated is an open-weight model for text generation from Seva, released under Apache License 2.0. It has 4B parameters and a 262,144-token context. At 16-bit it needs about 9.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
An abliterated version of Qwen/Qwen3-4B-Instruct-2507, made with Heretic. Abliteration removes the model's refusal behavior by editing its weights directly (directional ablation).
Parameters4B
Context262,144
Weights8.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve Qwen3-4B-Instruct-2507-abliterated (4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 8.0 GB | 9.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 4.0 GB | 4.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 2.0 GB | 2.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
Qwen3-4B-Instruct-2507-abliterated on every accelerator the SAVRN Index prices, at every precision
Model Card
By Seva, published under apache-2.0, revision 054f46dac6e9.
An abliterated version of Qwen/Qwen3-4B-Instruct-2507, made with Heretic. Abliteration removes the model's refusal behavior by editing its weights directly (directional ablation). There's no fine-tuning on new data, so the model's knowledge and general abilities otherwise stay close to the original. Works anywhere the base model does. With vLLM: Recommended sampling (from the base model): temperature=0.7, topp=0.8, topk=20. This model will comply with requests the original model would refuse. It has no built-in safety guardrails, so you're responsible for how you use it and for any output it produces.
Read Seva's full model card
An abliterated version of Qwen/Qwen3-4B-Instruct-2507, made with Heretic. Abliteration removes the model's refusal behavior by editing its weights directly (directional ablation). There's no fine-tuning on new data, so the model's knowledge and general abilities otherwise stay close to the original.
- Base model: Qwen3-4B-Instruct-2507 (4B parameters, non-thinking instruct model, 262k native context)
- Format: BF16 safetensors, same architecture and tokenizer as the base model
- License: Apache 2.0, inherited from the base model
Usage
Works anywhere the base model does. With vLLM:
vllm serve eccentricorbit/Qwen3-4B-Instruct-2507-abliterated
With Transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
name = "eccentricorbit/Qwen3-4B-Instruct-2507-abliterated"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "Hello!"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
Recommended sampling (from the base model): temperature=0.7, top_p=0.8, top_k=20.
Disclaimer
This model will comply with requests the original model would refuse. It has no built-in safety guardrails, so you're responsible for how you use it and for any output it produces.
Configuration
- Architecture
- Qwen3ForCausalLM
- Context length (tokens)
- 262,144
- Layers
- 36
- Hidden size
- 2,560
- Feed-forward size
- 9,728
- Attention heads
- 32
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- Model type
- qwen3
Identity and Version
- Repository
- glivorem/Qwen3-4B-Instruct-2507-abliterated
- Publisher
- Seva
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 4B parameters
- Languages
- en
- Revision
- 054f46dac6e9378f09aa99a3374e2ce827243d92
- First published
- 2026-10-16
- Last updated
- 2026-10-16
Files and Weights
10 files, 8.1 GB in total. The weights are 2 files totalling 8.0 GB in safetensors.
Weights2 files · 8.0 GB
Configuration3 files · 34.7 KB
Tokenizer2 files · 11.4 MB
Documentation1 file · 2.0 KB
Other1 file · 2.6 KB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00002.safetensors | Weights | 5.0 GB | 9dcb2a64608d |
| model-00002-of-00002.safetensors | Weights | 3.1 GB | cfd806ad9fba |
| config.json | Configuration | 1.6 KB | — |
| generation_config.json | Configuration | 213 B | — |
| model.safetensors.index.json | Configuration | 32.9 KB | — |
| README.md | Documentation | 2.0 KB | — |
| chat_template.jinja | Other | 2.6 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 11.4 MB | 79cb3c783570 |
| tokenizer_config.json | Tokenizer | 695 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 8.0 GB
Download from Seva
Released by Seva through its official repository on Hugging Face. Read the license.
Built From
- Derived from Qwen/Qwen3-4B-Instruct-2507
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 8.0 GB |
| 16-bit | 8.0 GB |
| 8-bit | 4.0 GB |
| 4-bit | 2.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Qwen3-4B-Instruct-2507-abliterated
How much GPU memory does Qwen3-4B-Instruct-2507-abliterated need?
About 9.7 GB at 16-bit and 2.4 GB at 4-bit: the weights (4B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Qwen3-4B-Instruct-2507-abliterated on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Qwen3-4B-Instruct-2507-abliterated commercially?
Yes. Qwen3-4B-Instruct-2507-abliterated is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Qwen3-4B-Instruct-2507-abliterated's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.
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