Quantization made by Richard Erkhov.

Mistral-7B-Instruct-v0.1 - GGUF

Model creator: https://huggingface.co/sanchit-gandhi/
Original model: https://huggingface.co/sanchit-gandhi/Mistral-7B-Instruct-v0.1/

Name	Quant method	Size
Mistral-7B-Instruct-v0.1.Q2_K.gguf	Q2_K	2.53GB
Mistral-7B-Instruct-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
Mistral-7B-Instruct-v0.1.Q3_K.gguf	Q3_K	3.28GB
Mistral-7B-Instruct-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
Mistral-7B-Instruct-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
Mistral-7B-Instruct-v0.1.IQ4_XS.gguf	IQ4_XS	3.67GB
Mistral-7B-Instruct-v0.1.Q4_0.gguf	Q4_0	3.83GB
Mistral-7B-Instruct-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
Mistral-7B-Instruct-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
Mistral-7B-Instruct-v0.1.Q4_K.gguf	Q4_K	4.07GB
Mistral-7B-Instruct-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
Mistral-7B-Instruct-v0.1.Q4_1.gguf	Q4_1	4.24GB
Mistral-7B-Instruct-v0.1.Q5_0.gguf	Q5_0	4.65GB
Mistral-7B-Instruct-v0.1.Q5_K_S.gguf	Q5_K_S	4.65GB
Mistral-7B-Instruct-v0.1.Q5_K.gguf	Q5_K	4.78GB
Mistral-7B-Instruct-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
Mistral-7B-Instruct-v0.1.Q5_1.gguf	Q5_1	5.07GB
Mistral-7B-Instruct-v0.1.Q6_K.gguf	Q6_K	5.53GB
Mistral-7B-Instruct-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 pipeline_tag: text-generation tags: - finetuned inference: parameters: temperature: 0.7

Model Card for Mistral-7B-Instruct-v0.1

This is a copy of Mistral's 7B-Instruct-v0.1 Large Language Model (LLM). Compared to the original version, who's weights are stored as 10GB shards, this version stores the weights in 5GB shards. Using smaller shards permits the use of the model on a Google Colab Free Tier without exceeding the system RAM (10GB). In all other ways, this model is identical to the original Mistral model.

The model is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

For full details of this model please read the paper and release blog post.

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Model Architecture

This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

Troubleshooting

If you see the following error:

Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'

Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers

This should not be required after transformers-v4.33.4.

Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

The Mistral AI Team

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.