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--- |
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base_model: BEE-spoke-data/Mixtral-GQA-400m-v2 |
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inference: false |
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language: |
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- en |
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license: apache-2.0 |
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model_creator: BEE-spoke-data |
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model_name: Mixtral-GQA-400m-v2 |
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pipeline_tag: text-generation |
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quantized_by: afrideva |
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tags: |
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- gguf |
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- ggml |
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- quantized |
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- q2_k |
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- q3_k_m |
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- q4_k_m |
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- q5_k_m |
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- q6_k |
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- q8_0 |
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--- |
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# BEE-spoke-data/Mixtral-GQA-400m-v2-GGUF |
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Quantized GGUF model files for [Mixtral-GQA-400m-v2](https://huggingface.co/BEE-spoke-data/Mixtral-GQA-400m-v2) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [mixtral-gqa-400m-v2.fp16.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.fp16.gguf) | fp16 | 4.01 GB | |
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| [mixtral-gqa-400m-v2.q2_k.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q2_k.gguf) | q2_k | 703.28 MB | |
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| [mixtral-gqa-400m-v2.q3_k_m.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q3_k_m.gguf) | q3_k_m | 899.86 MB | |
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| [mixtral-gqa-400m-v2.q4_k_m.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q4_k_m.gguf) | q4_k_m | 1.15 GB | |
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| [mixtral-gqa-400m-v2.q5_k_m.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q5_k_m.gguf) | q5_k_m | 1.39 GB | |
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| [mixtral-gqa-400m-v2.q6_k.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q6_k.gguf) | q6_k | 1.65 GB | |
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| [mixtral-gqa-400m-v2.q8_0.gguf](https://huggingface.co/afrideva/Mixtral-GQA-400m-v2-GGUF/resolve/main/mixtral-gqa-400m-v2.q8_0.gguf) | q8_0 | 2.13 GB | |
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## Original Model Card: |
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# BEE-spoke-data/Mixtral-GQA-400m-v2 |
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## testing code |
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```python |
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# !pip install -U -q transformers datasets accelerate sentencepiece |
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import pprint as pp |
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from transformers import pipeline |
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pipe = pipeline( |
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"text-generation", |
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model="BEE-spoke-data/Mixtral-GQA-400m-v2", |
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device_map="auto", |
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) |
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pipe.model.config.pad_token_id = pipe.model.config.eos_token_id |
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prompt = "My favorite movie is Godfather because" |
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res = pipe( |
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prompt, |
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max_new_tokens=256, |
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top_k=4, |
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penalty_alpha=0.6, |
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use_cache=True, |
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no_repeat_ngram_size=4, |
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repetition_penalty=1.1, |
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renormalize_logits=True, |
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) |
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pp.pprint(res[0]) |
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``` |
