From: allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject:

SERA-14B

Date: 2026-11-15Commit: 9b585b4Signed: 2DC28CC7B26A283B ✓

Model with 14.8B parameters. Safetensors weights, 29.6 GB in all. Published by allenai under Apache-2.0.

14.8B params29.6GBSERA-14Bsafetensorspermissive
FactsParams14.8BSize29.6 GBFormatssafetensorsTasknot taggedLicenseApache-2.0From sourcepermissive
$ fw get allenai/SERA-14B
# manifest sha256 c2d0b32a0560… · 29.6 GB · 19 files
$ fw verify ./SERA-14B allenai/SERA-14B
From sourceHosted (signed)allenai/SERA-14BLast check: 2026-11-15 04:01 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Works with

Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

Overview

Size
29.6 GB
Weights
29.5 GB (safetensors)
Parameters
14.8B
Formats
safetensors
Task
not tagged
License
Apache-2.0 (permissive)
Files
19, each with a sha256
Publisher
allenai
Added
2026-11-15
Section
alt.freeweights.olmo
Lineage

Original release from the publisher.

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Model card

From the publisher at commit 9b585b4. Shown as plain text with links; images open at the publisher.

SERA-14B#

SERA-14B is the fifth model in Ai2's Open Coding Agents series. It is a state-of-the-art 14B open-source coding agent that achieves 41.7% on SWE-bench Verified, outperforming or matching larger models like DeepSWE-Preview and SkyRL-Agent.

Model Variants#

ModelHuggingFaceBaseTeacherSWE-bench Verified
SERA-32Ballenai/SERA-32BQwen 3-32BGLM-4.649.5% ± 1.9%
SERA-32B-GAallenai/SERA-32B-GAQwen 3-32BGLM-4.5-Air46.6% ± 0.7%
SERA-14Ballenai/SERA-14BQwen 3-14BGLM-4.641.7% ± 0.5%
SERA-8Ballenai/SERA-8BQwen 3-8BGLM-4.631.7% ± 0.9%
SERA-8B-GAallenai/SERA-8B-GAQwen 3-8BGLM-4.5-Air31.7% ± 0.4%

All results evaluated at 32K context length. Standard deviations computed over 3 random seeds.

Performance#

SWE-bench Verified (32K Context)#
ModelTypeResolve Rate
SkyRL-8BOpen-source9.4%
Nex-N1-8BOpen-source20.3%
SERA-8BOpen-source31.7%
Qwen 3-32B (base)Open-weight24.4%
SWE-smithOpen-source32.6%
SkyRL-AgentOpen-source39.4%
SERA-14BOpen-source41.7%
DeepSWEOpen-source42.2%
SERA-32BOpen-source49.5%
Devstral-Small-2 (24B)Open-weight50.0%
GLM-4.5-Air (110B)Open-weight50.5%

Open-source: code, model weights, and data publicly available. Open-weight: model weights available but training data/code not fully released.

Quickstart#

The easiest way to use SERA is with the sera CLI, which provides seamless integration with Claude Code:

# Install the CLI
uv tool install ai2-sera-cli

# Option 1: Deploy on Modal (recommended for trying out)
modal setup  # one-time setup
sera --modal

# Option 2: Use an existing endpoint
export SERA_API_KEY=<your_api_key>
sera --endpoint <endpoint_url>

The first run with --modal takes approximately 10 minutes to download the model (~65GB) and compile. Subsequent runs start in 1-2 minutes.

For more deployment options, see the sera-cli documentation.

Self Hosting#

vllm serve allenai/SERA-14B --port 8001 \
            --tensor-parallel-size 4 \
            --max-model-len 32768 \
            --trust-remote-code \
            --enable-auto-tool-choice \
            --tool-call-parser hermes \
            --enforce-eager \
            --seed 42 \
            --disable-cascade-attn

Model Details#

DeveloperAllen Institute for AI (Ai2)
AuthorsEthan Shen, Daniel Tormoen, Saurabh Shah, Ali Farhadi, Tim Dettmers
Base ModelQwen 3-14B
Teacher ModelGLM-4.6 (357B)
Model TypeCoding agent / Software engineering agent
Training MethodSupervised fine-tuning on synthetic agent trajectories
Context Length32K tokens
LicenseApache 2.0
Training Configuration#
Epochs3
Learning Rate1e-5
Weight Decay0.01
Max Sequence Length32,768 tokens
Training FrameworkAxolotl
Inference FrameworkvLLM

Training Data#

SERA-14B is trained on 25,000 synthetic coding agent trajectories generated using Soft Verified Generation (SVG). SVG is a two-rollout pipeline:

  1. First rollout: A teacher model makes a change to a codebase starting from a randomly selected function
  2. Synthetic PR: The trajectory is converted into a pull request description
  3. Second rollout: The teacher attempts to reproduce the change given only the PR description
  4. Soft verification: Patches are compared using line-level recall (no test execution required)

This approach removes the need for test infrastructure and enables data generation from any repository.

  • Source Repositories: 121 Python codebases
  • Teacher Model: GLM-4.6 (357B)

Intended Use#

  • Automated software engineering: Bug fixes, feature implementation, refactoring
  • Repository specialization: Fine-tune on private codebases to create specialized coding agents (~8,000 trajectories / $1,300)
  • Research: Studying coding agents, data generation methods, and agent behavior

Limitations#

  • SWE-bench training artifact: The model was trained on SWE-bench-style tasks and may attempt to call a nonexistent submit tool when finished editing. The sera-cli proxy handles this automatically.
  • Evaluation scope: Only validated on SWE-bench Verified (Python repositories). Performance on other languages or benchmarks is unknown.
  • Teacher bound: Performance is largely bounded by the teacher model (GLM-4.6) capability.
  • Statistical variance: Results computed over 3 seeds. Effects smaller than 2-3% should be interpreted with caution.
  • Model-specific: Experiments use Qwen 3 as the base model. Generalization to other model families is not validated.

Bias, Risks, and Limitations#

Like any language model without safety filtering, SERA can be prompted to generate harmful or insecure code. Users should be aware of the following risks:

  • Code security: May generate code with security vulnerabilities (e.g., injection attacks, insecure defaults). All generated code should be reviewed before deployment.
  • Accuracy: May produce incorrect or buggy code. Outputs should be tested and verified.
  • Inherited biases: May reflect biases present in the Qwen 3-14B base model and GLM-4.6 teacher model.
  • Misuse potential: Could potentially be used to generate malicious code or identify vulnerabilities for exploitation.

Responsible Use#

This model is intended for research and educational use. Users should adhere to Ai2's Responsible Use Guidelines. Key principles include:

  • Use the model for beneficial purposes
  • Review and test all generated code before deployment
  • Do not use to generate malicious software or exploit vulnerabilities
  • Consider the potential impact of automated code generation in your context

Hardware Requirements#

ConfigurationGPUNotes
Minimum1× 80GB GPU (A100, H100)32K context
Recommended1× H100Best performance

Quantization (AWQ, GPTQ) can reduce memory requirements if needed.

License#

This model is licensed under Apache 2.0. It is intended for research and educational use and may be used commercially in accordance with Ai2's Responsible Use Guidelines.

Citation#

@misc{shen2026serasoftverifiedefficientrepository,
      title={SERA: Soft-Verified Efficient Repository Agents}, 
      author={Ethan Shen and Danny Tormoen and Saurabh Shah and Ali Farhadi and Tim Dettmers},
      year={2026},
      eprint={2601.20789},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.20789}, 
}

Contact#

  • Email: ethans03@cs.washington.edu, dettmers@cmu.edu
  • Issues: GitHub Issues

SERA / Open Coding Agents - Disclaimer Text

Bias, Risks, and Limitations SERA-32B/SERA-14B/SERA-8B is an open coding agent model released for research and educational purposes without any safety filtering or safety tuning. As a research artifact, this model is not suitable for real-world use without significant human oversight. Like other coding agents, this model may propagate biases present in training data or generate incorrect or insecure code. Security risks include prompt injection and data leakage. Always verify code outputs and manage context windows to avoid disclosing sensitive data or information.

Bias, Risks, and Limitations Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified. License This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Files

19 files · 29.6 GBWeights 29.5 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 19 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md9.30 KBec4dd79e69511580c2c2b3bf2c33125c57a0980a55a1cb85f02336d4248c56f5
added_tokens.json707 Bc0284b582e14987fbd3d5a2cb2bd139084371ed9acbae488829a1c900833c680
config.json1.63 KB18138ed618ae7364a68a99e38e8e3a636f055f5bb8392884e930f05037e3116e
debug.log839 B9b735ade9b28a5de3dde68b60b0dcdd96b36ec056faabba640b0198e3a27a685
generation_config.json212 Bd29da6cf99ab16657f574c83547b8d65f58020c15e886f8ed7e873148d184354
merges.txt1.67 MB8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
model-00001-of-00006.safetensors4.98 GBd2b10dda8ee5f485b23128dc75f910d7d1f8efe808125a31b57667a4571b7aab
model-00002-of-00006.safetensors4.98 GB002927c031cd103449fd6e380d87360ab569dc62e79befc2a21e1c51cae68868
model-00003-of-00006.safetensors4.93 GB26e27aed6febe91d42ef27363bafc4875437cea8ead65500c4c50186e22fa467
model-00004-of-00006.safetensors4.98 GBbda394fca3120f2f1874086bf35f50ed0647ebad4b88b68597eb49f627a94334
model-00005-of-00006.safetensors4.93 GBe02a37aea5f8eb05c435361e75b7099242b5c90b5823659f8d94124a116b6ecf
model-00006-of-00006.safetensors4.73 GB60cfd56c6623bcdd2dcccf528ce3b6f48ae1185c47cf4745cd8717c88ed9ae08
model.safetensors.index.json36.5 KB4548f2d89a64f20c2e016300569f3b4d4f41806edd37f2a93f44ded687a1a076
special_tokens_map.json613 B76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd
tokenizer.json11.4 MBaeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
tokenizer_config.json9.73 KBd5d09f07b48c3086c508b30d1c9114bd1189145b74e982a265350c923acd8101
training_args.bin9.40 KBfa15f762359c7835186cd5e03a2ba343995b7f489d55c8baa2328645649f7fbc
vocab.json2.78 MBca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
9b585b4126 pinned now2026-11-15 04:01 UTC19 files · 29.6 GB

Use it

$ fw get allenai/SERA-14B
# manifest sha256 c2d0b32a0560… · 29.6 GB · 19 files
$ fw verify ./SERA-14B allenai/SERA-14B
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/allenai/SERA-14B.json --dir ./SERA-14B $ python3 fw.py verify ./SERA-14B ./allenai__SERA-14B.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download allenai/SERA-14B --revision 9b585b4126595e471eae9d2c59c8d73dd0fe9e7b --local-dir ./SERA-14B

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
ec4dd79e69511580c2c2b3bf2c33125c57a0980a55a1cb85f02336d4248c56f5
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject: SERA-14BDate: 2026-11-15Commit: 9b585b4Signed: 2DC28CC7B26A283B ✓
Upstream
allenai/SERA-14B
Pinned commit
9b585b4126595e471eae9d2c59c8d73dd0fe9e7b
Manifest
SERA-14B.json · .minisig
Manifest sha256
c2d0b32a05601c2b00a342e02591d637c6abed1a1b9e23384284f333cd292248
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:01 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/allenai/SERA-14B.json $ curl -O https://freeweights.allternit.com/manifests/allenai/SERA-14B.json.minisig $ minisign -Vm SERA-14B.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

No related models in this catalog.Nothing here lists this model as its parent, and it names no parent.
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download allenai/SERA-14B
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. SERA-14B.json. Provenance. Takedown history: none.
From: bartowski <huggingface.co/bartowski>Subject: allenai_Olmo-3-7B-Instruct-GGUF
From: bartowski <huggingface.co/bartowski>Subject: allenai_Olmo-3-7B-Think-GGUF
From: bartowski <huggingface.co/bartowski>Subject: allenai_Olmo-3.1-32B-Instruct-GGUF
From: bartowski <huggingface.co/bartowski>Subject: allenai_Olmo-3.1-32B-Think-GGUF
From: bartowski <huggingface.co/bartowski>Subject: allenai_olmOCR-2-7B-1025-GGUF
From: bartowski <huggingface.co/bartowski>Subject: allenai_olmOCR-7B-0225-preview-GGUF