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Model: janhq/Mistral-7B-Instruct-v0.1-GGUF Source: Original Platform
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---
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- finetuned
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inference:
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parameters:
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temperature: 0.7
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---
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# Model Card for Mistral-7B-Instruct-v0.1
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The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
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For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
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## Instruction format
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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.
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E.g.
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```
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text = "<s>[INST] What is your favourite condiment? [/INST]"
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"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> "
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"[INST] Do you have mayonnaise recipes? [/INST]"
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```
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This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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messages = [
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{"role": "user", "content": "What is your favourite condiment?"},
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{"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!"},
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{"role": "user", "content": "Do you have mayonnaise recipes?"}
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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## Model Architecture
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This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
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- Grouped-Query Attention
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- Sliding-Window Attention
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- Byte-fallback BPE tokenizer
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## Troubleshooting
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- If you see the following error:
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```
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Traceback (most recent call last):
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File "", line 1, in
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File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
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config, kwargs = AutoConfig.from_pretrained(
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File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
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config_class = CONFIG_MAPPING[config_dict["model_type"]]
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File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
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raise KeyError(key)
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KeyError: 'mistral'
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```
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Installing transformers from source should solve the issue
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pip install git+https://github.com/huggingface/transformers
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This should not be required after transformers-v4.33.4.
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## Limitations
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The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
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It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
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make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
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## The Mistral AI Team
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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.
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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