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