Instructions to use infly/INF-34B-Chat-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use infly/INF-34B-Chat-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="infly/INF-34B-Chat-AWQ", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("infly/INF-34B-Chat-AWQ", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use infly/INF-34B-Chat-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "infly/INF-34B-Chat-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "infly/INF-34B-Chat-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/infly/INF-34B-Chat-AWQ
- SGLang
How to use infly/INF-34B-Chat-AWQ 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 "infly/INF-34B-Chat-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "infly/INF-34B-Chat-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "infly/INF-34B-Chat-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "infly/INF-34B-Chat-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use infly/INF-34B-Chat-AWQ with Docker Model Runner:
docker model run hf.co/infly/INF-34B-Chat-AWQ
| # coding=utf-8 | |
| # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. | |
| # | |
| # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX | |
| # and OPT implementations in this library. It has been modified from its | |
| # original forms to accommodate minor architectural differences compared | |
| # to GPT-NeoX and OPT used by the Meta AI team that trained the model. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """INFLM model configuration.""" | |
| from transformers.models.llama.configuration_llama import LlamaConfig | |
| class INFLMConfig(LlamaConfig): | |
| model_type = "inflm" | |
| def __init__( | |
| self, | |
| layer_norm_eps=1e-5, | |
| **kwargs, | |
| ): | |
| self.layer_norm_eps = layer_norm_eps | |
| super().__init__( | |
| **kwargs, | |
| ) | |