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Description

This repo contains GGUF format model files for Mistral-7B-Instruct-v0.2.

Files Provided

Name Quant Bits File Size Remark
mistral-7b-instruct-v0.2.IQ3_XXS.gguf IQ3_XXS 3 3.02 GB 3.06 bpw quantization
mistral-7b-instruct-v0.2.IQ3_S.gguf IQ3_S 3 3.18 GB 3.44 bpw quantization
mistral-7b-instruct-v0.2.IQ3_M.gguf IQ3_M 3 3.28 GB 3.66 bpw quantization mix
mistral-7b-instruct-v0.2.Q4_0.gguf Q4_0 4 4.11 GB 3.56G, +0.2166 ppl
mistral-7b-instruct-v0.2.IQ4_NL.gguf IQ4_NL 4 4.16 GB 4.25 bpw non-linear quantization
mistral-7b-instruct-v0.2.Q4_K_M.gguf Q4_K_M 4 4.37 GB 3.80G, +0.0532 ppl
mistral-7b-instruct-v0.2.Q5_K_M.gguf Q5_K_M 5 5.13 GB 4.45G, +0.0122 ppl
mistral-7b-instruct-v0.2.Q6_K.gguf Q6_K 6 5.94 GB 5.15G, +0.0008 ppl
mistral-7b-instruct-v0.2.Q8_0.gguf Q8_0 8 7.70 GB 6.70G, +0.0004 ppl

Parameters

path type architecture rope_theta sliding_win max_pos_embed
mistralai/Mistral-7B-Instruct-v0.2 mistral MistralForCausalLM 1000000.0 null 32768

Original Model Card

Model Card for Mistral-7B-Instruct-v0.2

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.

For full details of this model please read our 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-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")

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, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

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