Llama-3-70B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. We evaluated Llama-3-70B-Synthia-v3.5 on a wide range of tasks using Language Model Evaluation Harness from EleutherAI. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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View details ↗Llama-3-8B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using Language Model Evaluation Harness from EleutherAI. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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View details ↗Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B was trained on the meta-llama/Meta-Llama-3-70B base. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible.
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View details ↗Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B-v0.2 was trained on the meta-llama/Meta-Llama-3-70B base. The change between v0.1 and this version, v0.2 is that v0.2 has undergone an additional step of uncensoring. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible. In v0.2, an uncensored dataset was also added to the mix.
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View details ↗Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-8B was trained on the meta-llama/Meta-Llama-3-8B base. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 1-epoch with a low learning rate to try and preserve its entropy as much as possible.
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View details ↗GGUF quantizations of **migtissera/Tess-4-27B** — an agentic, reasoning-native 27B model that **scales its deliberation to the difficulty of the task**: tight on the easy calls, deep on the hard ones. Built on **Qwen/Qwen3.6-27B**. > 👉 **Full model card, capabilities, and details:** **migtissera/Tess-4-27B** **LM Studio / Ollama** — load any text quant. For image input in **LM Studio**, keep `mmproj-Tess-4-27B-F16.gguf` in the **same folder** as the model and it's auto-detected. Use a recent llama.cpp runtime — older builds won't recognize the `qwen35` architecture.
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View details ↗Tess-4.5 is a 27-billion-parameter open-weights model for real engineering work. It reasons through every step, works with the tools and files actually in front of it, and runs on a single GPU or a laptop. On BenchLocal it matches its base, Qwen3.8-27B, with 25% fewer tokens. This repository holds the GGUF quantizations for llama.cpp, the vision projector and the multi-token-prediction draft heads. MTP speculative decoding needs a recent upstream llama.cpp build. On a 24 GB GPU at very long contexts, quantize the KV caches (`--cache-type-k q4_0 --cache-type-v q4_0`, and `-ctkd q4_0 -ctvd q4_0` for the draft) or lower `-c`. Tess works best when it knows where it is. State the working directory in the system prompt and attach your real tool definitions:
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View details ↗This version is the stable release. The issues that were present in versions 1.0, 1.1 and 1.2 all have been rectified. Thank you for your patience while R&D was conducted. Enjoy! This model have been tested on very long context length. Model produced slight repetition, but it was very minor. I recommend testing the model to your usecase and limiting the context length. Here's my conversation: https://migel.substack.com/p/testing-tess-m-v13 As can be seen, "USER:" and "SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation." was presented by the model in the latter part of the conversation.
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