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Create an .env file from .env.example\n2. Start with the desired model\n\n## Whisper\n\nhttps://github.com/openai/whisper\n\nWhisper is a general-purpose speech recognition model.\n\nWisper requires the command-line tool **ffmpeg** to be installed on your system, which is available from most package managers.\n\n## TTS\n\nhttps://github.com/coqui-ai/TTS\n\nTTS is a super cool Text-to-Speech model that lets you clone voices in different languages by using just a quick 3-second audio clip.\n\nThe bot responds with a voice to a voice message.\n\nenv\n**TTS_SPEAKER** - available speaker or custome voice (.wav). If no value is set, voice response is disabled.\n**TTS_LANGUAGE** - answer text language\n\n## Ollama\n\nhttps://github.com/jmorganca/ollama\n\n1. Pull and run ollama/ollama docker image\n2. Download model (e.g. mistral)\n3. Run telegram chat\n\n```\npython3 main.py -c ollama -u http://localhost:11434/api -m mistral\n```\n\n## GPT4All\n\nhttps://github.com/nomic-ai/gpt4all\n\n1. Clone gpt4all-api\n2. Run the docker build (see GPT4All documentation)\n   1. I recommend modifying the gpt4all_api/Dockerfile.buildkit file to remove the `RUN wget` command\n   2. Add `volume` \u003clocal_path_to_dir_with_models\u003e:/models in `docker-compose.yaml`\n   3. Download some models to \u003clocal_path_to_dir_with_models\u003e\n3. Run the server (see GPT4All documentation)\n   - Use your model for `model` var in environment `docker-compose.yaml`\n4. Run telegram chat\n\n```\npython3 main.py --client=gpt4all --url=http://localhost:4891/v1 --model=nous-hermes-llama2-13b.Q4_0.gguf\n```\n\n\u003e `--client=gpt4all` is specified to use the `Completion` method for Gpt4All. Cause `ChatCompletion` doesn't work for me. You can add some logic to the /gpt4all-api/gpt4all_api/app/api_v1/routes/chat.py in the GPT4All repo. Then run bot-ai without --client=gpt4all.\n\nOpenapi doc: `http://localhost:4891/docs`\n\n## Fastchat\n\nhttps://github.com/lm-sys/FastChat\n\n1. Clone repo\n2. Install dependencies\n3. Start the api server (see Fastchat documentation)\n\ne.g.\n\n```\npython3 -m fastchat.serve.controller\n\npython3 -m fastchat.serve.model_worker --model-path ../Models/vicuna-7b-1.5 --load-8bit --cpu-offloading\n\npython3 -m fastchat.serve.openai_api_server --host localhost --port 4891\n```\n\n`../Models/vicuna-7b-1.5` - downloaded model\n\n`--load-8bit` and `--cpu-offloading` parameters are used to reduce the load on the system.\n\n4. Run telegram chat\n\n```\npython3 main.py -u http://localhost:4891/v1 -m vicuna-7b-1.5\n```\n\nOpenapi doc: `http://localhost:4891/docs`","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fifeech%2Ftelegrambot-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fifeech%2Ftelegrambot-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fifeech%2Ftelegrambot-ai/lists"}