YAML Metadata
Warning:
The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
cypienai/cymist-2-v02-SFT - GGUF
This repo contains GGUF format model files for cypienai/cymist-2-v02-SFT.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Our projects
| Forge | |
|---|---|
|
|
| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
 |
 |
| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
<s>[INST] {prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| cymist-2-v02-SFT-Q2_K.gguf | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes |
| cymist-2-v02-SFT-Q3_K_S.gguf | Q3_K_S | 2.947 GB | very small, high quality loss |
| cymist-2-v02-SFT-Q3_K_M.gguf | Q3_K_M | 3.277 GB | very small, high quality loss |
| cymist-2-v02-SFT-Q3_K_L.gguf | Q3_K_L | 3.560 GB | small, substantial quality loss |
| cymist-2-v02-SFT-Q4_0.gguf | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| cymist-2-v02-SFT-Q4_K_S.gguf | Q4_K_S | 3.856 GB | small, greater quality loss |
| cymist-2-v02-SFT-Q4_K_M.gguf | Q4_K_M | 4.068 GB | medium, balanced quality - recommended |
| cymist-2-v02-SFT-Q5_0.gguf | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| cymist-2-v02-SFT-Q5_K_S.gguf | Q5_K_S | 4.654 GB | large, low quality loss - recommended |
| cymist-2-v02-SFT-Q5_K_M.gguf | Q5_K_M | 4.779 GB | large, very low quality loss - recommended |
| cymist-2-v02-SFT-Q6_K.gguf | Q6_K | 5.534 GB | very large, extremely low quality loss |
| cymist-2-v02-SFT-Q8_0.gguf | Q8_0 | 7.167 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/cymist-2-v02-SFT-GGUF --include "cymist-2-v02-SFT-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/cymist-2-v02-SFT-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
- 120
Hardware compatibility
Log In
to view the estimation
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Model tree for tensorblock/cymist-2-v02-SFT-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.070
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.430
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard52.060
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard38.970
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.610
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.070