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redscroll_-_Mistral-7B-v0.2-gguf
RichardErkhov/redscroll_-_Mistral-7B-v0.2-gguf
redscroll_-_Mistral-7B-v0.2-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- Sep 17, 2024
- Updated
- Sep 17, 2024
- SHA
- 1a286699
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Mistral-7B-v0.2 - GGUF
- Model creator: https://huggingface.co/redscroll/
- Original model: https://huggingface.co/redscroll/Mistral-7B-v0.2/
| Name | Quant method | Size |
|---|---|---|
| Mistral-7B-v0.2.Q2_K.gguf | Q2_K | 2.53GB |
| Mistral-7B-v0.2.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Mistral-7B-v0.2.IQ3_S.gguf | IQ3_S | 2.96GB |
| Mistral-7B-v0.2.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Mistral-7B-v0.2.IQ3_M.gguf | IQ3_M | 3.06GB |
| Mistral-7B-v0.2.Q3_K.gguf | Q3_K | 3.28GB |
| Mistral-7B-v0.2.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Mistral-7B-v0.2.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Mistral-7B-v0.2.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Mistral-7B-v0.2.Q4_0.gguf | Q4_0 | 3.83GB |
| Mistral-7B-v0.2.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Mistral-7B-v0.2.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Mistral-7B-v0.2.Q4_K.gguf | Q4_K | 4.07GB |
| Mistral-7B-v0.2.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Mistral-7B-v0.2.Q4_1.gguf | Q4_1 | 4.24GB |
| Mistral-7B-v0.2.Q5_0.gguf | Q5_0 | 4.65GB |
| Mistral-7B-v0.2.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Mistral-7B-v0.2.Q5_K.gguf | Q5_K | 4.78GB |
| Mistral-7B-v0.2.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Mistral-7B-v0.2.Q5_1.gguf | Q5_1 | 5.07GB |
| Mistral-7B-v0.2.Q6_K.gguf | Q6_K | 5.53GB |
| Mistral-7B-v0.2.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
pipeline_tag: text-generation
Not my model(obviously); downloaded the Mistral release model from https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar and uploaded for my own sanity(and fine-tuning), since it's still not uploaded on Mistral repo.
The standard code works:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("redscroll/Mistral-7B-v0.2", torch_dtype=torch.bfloat16, device_map = "auto")
tokenizer = AutoTokenizer.from_pretrained("redscroll/Mistral-7B-v0.2")
input_text = "In my younger and more vulnerable years"
input_ids = tokenizer(input_text, return_tensors = "pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens = 500, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
- Created
- Sep 17, 2024
- Updated
- Sep 17, 2024