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adamksn/Mistral-7B-v0.1-GGUF
Mistral-7B-v0.1-GGUF is a text generation model from adamksn. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
This repo contains GGUF format model files for Mistral-7B-v0.1.
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From the Hugging Face model README
This repo contains GGUF format model files for Mistral-7B-v0.1.
| Name | Quant | Bits | File Size | Remark |
|---|---|---|---|---|
| mistral-7b-v0.1.IQ3_XXS.gguf | IQ3_XXS | 3 | 3.02 GB | 3.06 bpw quantization |
| mistral-7b-v0.1.IQ3_S.gguf | IQ3_S | 3 | 3.18 GB | 3.44 bpw quantization |
| mistral-7b-v0.1.IQ3_M.gguf | IQ3_M | 3 | 3.28 GB | 3.66 bpw quantization mix |
| mistral-7b-v0.1.IQ4_NL.gguf | IQ4_NL | 4 | 4.16 GB | 4.25 bpw non-linear quantization |
| mistral-7b-v0.1.Q4_K_M.gguf | Q4_K_M | 4 | 4.37 GB | 3.80G, +0.0532 ppl |
| mistral-7b-v0.1.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | 4.45G, +0.0122 ppl |
| mistral-7b-v0.1.Q6_K.gguf | Q6_K | 6 | 5.94 GB | 5.15G, +0.0008 ppl |
| mistral-7b-v0.1.Q8_0.gguf | Q8_0 | 8 | 7.70 GB | 6.70G, +0.0004 ppl |
| path | type | architecture | rope_theta | sliding_win | max_pos_embed |
|---|---|---|---|---|---|
| mistralai/Mistral-7B-v0.1 | mistral | MistralForCausalLM | 10000.0 | 4096 | 32768 |
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our paper and release blog post.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
KeyError: 'mistral'
NotImplementedError: Cannot copy out of meta tensor; no data!
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.