mirror of
https://github.com/dogkeeper886/ollama37.git
synced 2025-12-19 12:17:02 +00:00
move back to root
This commit is contained in:
Bruce MacDonald
@@ -1,19 +0,0 @@
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import site
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import os
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from PyInstaller.__main__ import run as pyi_run
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# the llama_cpp directory is not included if not explicitly added
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site_packages_dir = site.getsitepackages()[0]
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llama_cpp_dir = os.path.join(site_packages_dir, "llama_cpp")
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args = [
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"ollama.py",
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"--paths",
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site_packages_dir,
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"--add-data",
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f"{llama_cpp_dir}{os.pathsep}llama_cpp",
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"--onefile"
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]
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# generate the .spec file and run PyInstaller
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pyi_run(args)
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@@ -1,12 +0,0 @@
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{
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"alpaca": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:\n\n",
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"ggml": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n### Human: Hello, Assistant.\n### Assistant: Hello. How may I help you today?\n### Human: ${prompt}",
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"gpt4": "### Instruction:\n{prompt}\n\n### Response:\n",
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"hermes": "### Instruction:\n{prompt}\n\n### Response:\n",
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"oasst": "{prompt}",
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"orca": "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n### User:\n{prompt}\n\n### Response:",
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"qlora": "### Human: {prompt}\n### Assistant:",
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"tulu": "\n{prompt}\n\n(include newline)",
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"vicuna": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUSER: {prompt}\nASSISTANT:",
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"wizardlm": "{prompt}\n\n### Response:"
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}
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223
ollama/ollama.py
223
ollama/ollama.py
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import json
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import os
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import threading
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import click
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from transformers import AutoModel
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from tqdm import tqdm
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from pathlib import Path
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from llama_cpp import Llama
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from flask import Flask, Response, stream_with_context, request
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from flask_cors import CORS
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from template import template
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app = Flask(__name__)
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CORS(app) # enable CORS for all routes
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# llms tracks which models are loaded
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llms = {}
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lock = threading.Lock()
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def models_directory():
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home_dir = Path.home()
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models_dir = home_dir / ".ollama/models"
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if not models_dir.exists():
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models_dir.mkdir(parents=True)
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return models_dir
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def load(model=None, path=None):
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"""
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Load a model.
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The model can be specified by providing either the path or the model name,
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but not both. If both are provided, this function will raise a ValueError.
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If the model does not exist or could not be loaded, this function returns an error.
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Args:
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model (str, optional): The name of the model to load.
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path (str, optional): The path to the model file.
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Returns:
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dict or None: If the model cannot be loaded, a dictionary with an 'error' key is returned.
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If the model is successfully loaded, None is returned.
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"""
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with lock:
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if path is not None and model is not None:
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raise ValueError(
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"Both path and model are specified. Please provide only one of them."
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)
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elif path is not None:
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name = os.path.basename(path)
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load_from = path
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elif model is not None:
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name = model
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dir = models_directory()
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load_from = str(dir / f"{model}.bin")
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else:
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raise ValueError("Either path or model must be specified.")
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if not os.path.exists(load_from):
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return {"error": f"The model at {load_from} does not exist."}
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if name not in llms:
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# TODO: download model from a repository if it does not exist
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llms[name] = Llama(model_path=load_from)
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# TODO: this should start a persistent instance of ollama with the model loaded
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return None
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def unload(model):
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"""
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Unload a model.
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Remove a model from the list of loaded models. If the model is not loaded, this is a no-op.
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Args:
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model (str): The name of the model to unload.
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"""
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llms.pop(model, None)
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def generate(model, prompt):
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# auto load
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error = load(model)
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print(error)
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if error is not None:
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return error
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generated = llms[model](
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str(prompt), # TODO: optimize prompt based on model
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max_tokens=4096,
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stop=["Q:", "\n"],
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stream=True,
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)
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for output in generated:
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yield json.dumps(output)
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def models():
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dir = models_directory()
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all_files = os.listdir(dir)
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bin_files = [
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file.replace(".bin", "") for file in all_files if file.endswith(".bin")
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]
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return bin_files
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@app.route("/load", methods=["POST"])
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def load_route_handler():
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data = request.get_json()
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model = data.get("model")
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if not model:
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return Response("Model is required", status=400)
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error = load(model)
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if error is not None:
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return error
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return Response(status=204)
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@app.route("/unload", methods=["POST"])
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def unload_route_handler():
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data = request.get_json()
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model = data.get("model")
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if not model:
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return Response("Model is required", status=400)
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unload(model)
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return Response(status=204)
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@app.route("/generate", methods=["POST"])
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def generate_route_handler():
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data = request.get_json()
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model = data.get("model")
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prompt = data.get("prompt")
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if not model:
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return Response("Model is required", status=400)
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if not prompt:
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return Response("Prompt is required", status=400)
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if not os.path.exists(f"{model}"):
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return {"error": "The model does not exist."}, 400
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return Response(
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stream_with_context(generate(model, prompt)), mimetype="text/event-stream"
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)
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@app.route("/models", methods=["GET"])
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def models_route_handler():
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bin_files = models()
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return Response(json.dumps(bin_files), mimetype="application/json")
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@click.group(invoke_without_command=True)
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@click.pass_context
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def cli(ctx):
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# allows the script to respond to command line input when executed directly
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if ctx.invoked_subcommand is None:
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click.echo(ctx.get_help())
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@cli.command()
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@click.option("--port", default=5000, help="Port to run the server on")
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@click.option("--debug", default=False, help="Enable debug mode")
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def serve(port, debug):
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print("Serving on http://localhost:{port}")
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app.run(host="0.0.0.0", port=port, debug=debug)
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@cli.command(name="load")
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@click.argument("model")
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@click.option("--file", default=False, help="Indicates that a file path is provided")
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def load_cli(model, file):
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if file:
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error = load(path=model)
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else:
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error = load(model)
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if error is not None:
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print(error)
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return
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print("Model loaded")
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@cli.command(name="generate")
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@click.argument("model")
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@click.option("--prompt", default="", help="The prompt for the model")
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def generate_cli(model, prompt):
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if prompt == "":
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prompt = input("Prompt: ")
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output = ""
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prompt = template(model, prompt)
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for generated in generate(model, prompt):
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generated_json = json.loads(generated)
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text = generated_json["choices"][0]["text"]
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output += text
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print(f"\r{output}", end="", flush=True)
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def download_model(model_name):
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dir = models_directory()
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AutoModel.from_pretrained(model_name, cache_dir=dir)
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@cli.command(name="models")
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def models_cli():
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print(models())
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@cli.command(name="pull")
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@click.argument("model")
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def pull_cli(model):
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print("not implemented")
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@cli.command(name="import")
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@click.argument("model")
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def import_cli(model):
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print("not implemented")
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if __name__ == "__main__":
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cli()
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@@ -1,5 +0,0 @@
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Flask==2.3.2
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flask_cors==3.0.10
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llama-cpp-python==0.1.65
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pyinstaller==5.13.0
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pyinstaller-hooks-contrib==2023.3
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@@ -1,20 +0,0 @@
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from difflib import SequenceMatcher
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import json
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with open("model_prompts.json", "r") as f:
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model_prompts = json.load(f)
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def template(model, prompt):
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max_ratio = 0
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closest_key = ""
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model_name = model.lower()
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# Find the specialized prompt with the closest name match
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for key in model_prompts.keys():
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ratio = SequenceMatcher(None, model_name, key).ratio()
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if ratio > max_ratio:
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max_ratio = ratio
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closest_key = key
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# Return the value of the closest match
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p = model_prompts.get(closest_key) # TODO: provide a better default template
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return p.format(prompt=prompt)
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