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ling-3.0-flash-iq1
Ling-3.0-flash is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 124B total parameters and 5.5B active parameters per token. It targets
coding, deep research, instruction following, and agentic workflows with a
native 256K-token context window.
This default entry uses the 36.5 GB AD-IQ1_M GGUF. A higher-quality
44.7 GB AD-IQ2_XS model is available as a variant.
Repository: localaiLicense: mit
nemotron-3-embed-1b-q4
Nemotron-3-Embed-1B is NVIDIA's multilingual text embedding model for
retrieval, semantic search, and RAG. This compact Q4_K_M GGUF produces
2,048-dimensional normalized embeddings and supports 36 languages. Prefix
retrieval queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
nemotron-3-embed-8b-q4
Nemotron-3-Embed-8B is NVIDIA's larger multilingual text embedding model
for retrieval, semantic search, and RAG. This Q4_K_M GGUF balances retrieval
quality with local resource use and supports 36 languages. Prefix retrieval
queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
inkling
# Inkling
BF16 |
NVFP4 |
Playground |
Tinker Cookbook |
Acceptable Use
## 1. General Information
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.
**Languages:** English, with general multilingual capabilities across other languages.
## 2. Getting Started
Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook.
Inkling supports local deployment using the following open-source libraries:
* SGLang (recipe, PR)
* vLLM (recipe, PR)
* TokenSpeed (recipe, PR)
* Unsloth (recipe, PR)
* Huggingface (recipe, PR)
...
Repository: localaiLicense: apache-2.0
qwythos-9b-v2
Empero AI
# Qwythos-9B-v2 — the new and improved Qwythos
The next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use.
- 🔁 **Looping behavior eliminated** — repetition/degeneration under greedy or low-temperature decoding dropped from **6.7% → 0%**. You can serve it *without* leaning on `repetition_penalty` as a band-aid.
- 🧠 **Reasoning fully preserved** — MMLU, GSM8K, GPQA, ARC and HumanEval are all held at (or above) the v1 level. This is a *hygiene* upgrade, not a capability regression.
- 🧩 **MTP head restored** — the native multi-token-prediction module (dropped in the previous export) is back, so config and weights agree and speculative-decoding setups work.
- 🪪 **Cleaner identity** — the model no longer prefaces unrelated answers with its identity; it introduces itself only when you actually ask.
- 🔓 **Still intentionally uncensored** for research, cybersecurity, red-teaming, biology, chemistry, pharmacology and clinical work.
- 📜 **St
...
Repository: localaiLicense: apache-2.0
ornith-1.0-9b-mtp
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-9B
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 9B
This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B
Agentic Coding
...
Repository: localaiLicense: mit
agents-a1-4b
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q4_K_M GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
agents-a1-4b-q8
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q8_0 GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
ornith-1.0-9b
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-9B-GGUF
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 9B
This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B
Agentic Coding
...
Repository: localaiLicense: mit
ornith-1.0-35b
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-35B-GGUF
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 35B
This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-35B
Qwen3.5-35B
Qwen3.6-35B
Gemma4-31B
Qwen3.5-397B
Agentic Coding
...
Repository: localaiLicense: mit
bigbang-v1-q4-k-m
BigBang-v1 is an Apache-2.0 multimodal reasoning model fine-tuned from
Qwen3.6-35B-A3B for scientific research, coding, long-horizon search, and
tool use. It supports image input, a 262K native context window, and
built-in multi-token prediction. This entry uses the recommended Q4_K_M
GGUF quantization and the F16 vision projector.
Repository: localaiLicense: apache-2.0
bigbang-v1-q6-k
BigBang-v1 is an Apache-2.0 multimodal reasoning model fine-tuned from
Qwen3.6-35B-A3B for scientific research, coding, long-horizon search, and
tool use. It supports image input, a 262K native context window, and
built-in multi-token prediction. This entry uses the higher-quality Q6_K
GGUF quantization and the F16 vision projector.
Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-qat
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
...
Repository: localaiLicense: apache-2.0
gemma-4-12b-it-qat-q4_0
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
...
Repository: localaiLicense: apache-2.0
zero-gemma4-e4b-openzero-q5-k-m
Zero Gemma4-E4B OpenZero is a Gemma 4 E4B instruction fine-tune for
local coding, research, chat, and agentic workflows. It was trained on
2,033 curated OpenZero examples and is distributed as one merged Q5_K_M
GGUF file.
License: OpenZero Community Source v1; see the model repository for terms.
Repository: localaiLicense: other
step-3.7-flash
**[ModelPage]**: https://static.stepfun.com/blog/step-3.7-flash/
## 1. Introduction
Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model that combines a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding. Engineered for high-frequency production workloads, it activates approximately 11B parameters per token and delivers a throughput of up to 400 tokens per second. Step 3.7 Flash supports a 256k context window and offers three selectable reasoning levels (low, medium, and high) so developers can easily balance speed, cost, and cognitive depth.
We built Step 3.7 Flash for developers who need to scale agentic workflows that combine perception, search, and reasoning. It is designed to handle intensive tasks such as parsing massive financial reports in one pass, running multi-step search loops with cross-source verification, or operating concurrent coding agents in high-throughput pipelines.
## 2. Capabilities & Performance
### Multimodal Perception and Verification
...
Repository: localaiLicense: apache-2.0
xyz-aquila-mini
XYZ-Aquila-mini is an Apache-2.0, open-weight thinking model based on
Qwen3.6-35B-A3B. It is tuned for agentic deep search, long-horizon planning,
bilingual web research, evidence aggregation, source verification, and
recovery from failed tool interactions. This entry uses the Q4_K_M GGUF;
the Q6_K variant offers higher fidelity on hosts with more memory.
The checkpoint supports Qwen-compatible reasoning and tool-call formats.
Search, scraping, and Python execution are supplied by the agent harness,
not by the model weights themselves.
Repository: localaiLicense: apache-2.0
xyz-aquila-mini-q6
Higher-fidelity Q6_K variant of XYZ-Aquila-mini, an Apache-2.0 Qwen3.6
MoE thinking model tuned for agentic deep search and tool use.
Repository: localaiLicense: apache-2.0
chroma1-hd
Chroma1-HD is an 8.9B-parameter text-to-image foundation model derived from FLUX.1-schnell with reduced parameter count via architectural optimizations. Designed as a base for creators, researchers, and downstream fine-tuning. Recommended inference: 40 steps, CFG 3.0, bfloat16.
Repository: localaiLicense: apache-2.0
arex-turbo
AREX-Turbo is BAAI's compact 4B deep-research agent, fine-tuned from
Qwen3.5-4B for long-horizon search, evidence aggregation, constraint
verification, and tool-assisted reasoning. It supports text and image
input with a 262K-token context window. This entry uses the recommended
Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
arex-turbo-q8
AREX-Turbo is BAAI's compact 4B deep-research agent, fine-tuned from
Qwen3.5-4B for long-horizon search, evidence aggregation, constraint
verification, and tool-assisted reasoning. It supports text and image
input with a 262K-token context window. This entry uses the higher-quality
Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
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