Text Generation
Transformers
Safetensors
English
autoscientist
marketing
instruction-tuning
mixtral
copywriting
advertising
email-marketing
product-description
brandvoice
Instructions to use suehuynh/Marketing-Mixtral-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suehuynh/Marketing-Mixtral-8x7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="suehuynh/Marketing-Mixtral-8x7B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("suehuynh/Marketing-Mixtral-8x7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use suehuynh/Marketing-Mixtral-8x7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "suehuynh/Marketing-Mixtral-8x7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "suehuynh/Marketing-Mixtral-8x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/suehuynh/Marketing-Mixtral-8x7B
- SGLang
How to use suehuynh/Marketing-Mixtral-8x7B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "suehuynh/Marketing-Mixtral-8x7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "suehuynh/Marketing-Mixtral-8x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "suehuynh/Marketing-Mixtral-8x7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "suehuynh/Marketing-Mixtral-8x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use suehuynh/Marketing-Mixtral-8x7B with Docker Model Runner:
docker model run hf.co/suehuynh/Marketing-Mixtral-8x7B
Delete adaption_mixtral_8x7b_instruct_marketing_tasks
Browse files- adaption_mixtral_8x7b_instruct_marketing_tasks/README.md +0 -202
- adaption_mixtral_8x7b_instruct_marketing_tasks/adapter_config.json +0 -36
- adaption_mixtral_8x7b_instruct_marketing_tasks/adapter_model.safetensors +0 -3
- adaption_mixtral_8x7b_instruct_marketing_tasks/chat_template.jinja +0 -24
- adaption_mixtral_8x7b_instruct_marketing_tasks/config.json +0 -36
- adaption_mixtral_8x7b_instruct_marketing_tasks/tokenizer.json +0 -0
- adaption_mixtral_8x7b_instruct_marketing_tasks/tokenizer_config.json +0 -19
- adaption_mixtral_8x7b_instruct_marketing_tasks/trainer_state.json +0 -368
adaption_mixtral_8x7b_instruct_marketing_tasks/README.md
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base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
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library_name: peft
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# Model Card for Model ID
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## Model Details
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### Model Description
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Details
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### Training Data
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Citation [optional]
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## More Information [optional]
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### Framework versions
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"r": 16,
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"rank_pattern": {},
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version https://git-lfs.github.com/spec/v1
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{%- if messages[0]['role'] == 'system' %}
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{{- bos_token }}
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{%- for message in loop_messages %}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
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{{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}
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{%- endif %}
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{%- if message['role'] == 'user' %}
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{%- if loop.first and system_message is defined %}
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{{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}
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{%- else %}
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{{- ' [INST] ' + message['content'] + ' [/INST]' }}
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{%- endif %}
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{%- elif message['role'] == 'assistant' %}
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{{- ' ' + message['content'] + eos_token}}
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{%- else %}
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| 22 |
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{{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}
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{%- endif %}
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{%- endfor %}
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adaption_mixtral_8x7b_instruct_marketing_tasks/config.json
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adaption_mixtral_8x7b_instruct_marketing_tasks/tokenizer.json
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adaption_mixtral_8x7b_instruct_marketing_tasks/tokenizer_config.json
DELETED
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