Merge pull request #12 from jmorganca/prompt-templates-redux

add prompt templates as j2 templates
2023-06-28 11:53:50 -07:00 · 2023-06-28 11:53:50 -07:00 · 50f5adb09b
commit 50f5adb09b
parent ce7fe42345 dd4448f787
16 changed files with 194 additions and 440 deletions
--- a/ollama/cmd/cli.py
+++ b/ollama/cmd/cli.py
@ -69,6 +69,7 @@ def generate_oneshot(*args, **kwargs):
        if len(choices) > 0:
            print(choices[0].get("text", ""), end="", flush=True)

+    # end with a new line
    print()


--- a/ollama/engine.py
+++ b/ollama/engine.py
@ -3,9 +3,9 @@ import json
 import sys
 from contextlib import contextmanager
 from llama_cpp import Llama as LLM
-from template import template

 import ollama.model
+import ollama.prompt


@contextmanager
@ -21,7 +21,7 @@ def suppress_stderr():
 def generate(model, prompt, models_home=".", llms={}, *args, **kwargs):
    llm = load(model, models_home=models_home, llms=llms)

-    prompt = template(model, prompt)
+    prompt = ollama.prompt.template(model, prompt)

    if "max_tokens" not in kwargs:
        kwargs.update({"max_tokens": 16384})
@ -43,12 +43,9 @@ def load(model, models_home=".", llms={}):
            name: path for name, path in ollama.model.models(models_home)
        }.get(model, None)

-        if model_path is None:
+        if not model_path:
            # try loading this as a path to a model, rather than a model name
-            if os.path.isfile(model):
            model_path = model
-            else:
-                raise ValueError("Model not found")

        # suppress LLM's output
        with suppress_stderr():
--- a/ollama/prompt.py
+++ b/ollama/prompt.py
@ -0,0 +1,19 @@
+from os import path
+from difflib import SequenceMatcher
+from jinja2 import Environment, PackageLoader
+
+
+def template(model, prompt):
+    best_ratio = 0
+    best_template = ''
+
+    environment = Environment(loader=PackageLoader(__name__, 'templates'))
+    for template in environment.list_templates():
+        base, _ = path.splitext(template)
+        ratio = SequenceMatcher(None, model.lower(), base).ratio()
+        if ratio > best_ratio:
+            best_ratio = ratio
+            best_template = template
+
+    template = environment.get_template(best_template)
+    return template.render(prompt=prompt)
--- a/ollama/templates/alpaca.prompt
+++ b/ollama/templates/alpaca.prompt
@ -0,0 +1,8 @@
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{ prompt }}
+
+### Response:
+
+
--- a/ollama/templates/gpt4.prompt
+++ b/ollama/templates/gpt4.prompt
@ -0,0 +1,5 @@
+### Instruction:
+{{ prompt }}
+
+### Response:
+
--- a/ollama/templates/hermes.prompt
+++ b/ollama/templates/hermes.prompt
@ -0,0 +1,5 @@
+### Instruction:
+{{ prompt }}
+
+### Response:
+
--- a/ollama/templates/oasst.prompt
+++ b/ollama/templates/oasst.prompt
@ -0,0 +1 @@
+{{ prompt }}
--- a/ollama/templates/orca.prompt
+++ b/ollama/templates/orca.prompt
@ -0,0 +1,7 @@
+### System:
+You are an AI assistant that follows instruction extremely well. Help as much as you can.
+
+### User:
+{{ prompt }}
+
+### Response:
--- a/ollama/templates/qlora.prompt
+++ b/ollama/templates/qlora.prompt
@ -0,0 +1,2 @@
+### Human: {{ prompt }}
+### Assistant:
--- a/ollama/templates/tulu.prompt
+++ b/ollama/templates/tulu.prompt
@ -0,0 +1,4 @@
+
+{{ prompt }}
+
+
--- a/ollama/templates/vicuna.prompt
+++ b/ollama/templates/vicuna.prompt
@ -0,0 +1,4 @@
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+
+USER: {{ prompt }}
+ASSISTANT:
--- a/ollama/templates/wizardlm.prompt
+++ b/ollama/templates/wizardlm.prompt
@ -0,0 +1,2 @@
+{{ prompt }}
+### Response:
--- a/poetry.lock
+++ b/poetry.lock
@ -422,6 +422,23 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]

+[[package]]
+name = "jinja2"
+version = "3.1.2"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
+    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
 [[package]]
 name = "llama-cpp-python"
 version = "0.1.66"
@ -440,6 +457,65 @@ typing-extensions = ">=4.6.3,<5.0.0"
 [package.extras]
 server = ["fastapi (>=0.97.0,<0.98.0)", "sse-starlette (>=1.6.1,<2.0.0)", "uvicorn (>=0.22.0,<0.23.0)"]

+[[package]]
+name = "markupsafe"
+version = "2.1.3"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "MarkupSafe-2.1.3.tar.gz", hash = "sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad"},
+]
+
 [[package]]
 name = "mccabe"
 version = "0.7.0"
@ -950,4 +1026,4 @@ server = ["aiohttp", "aiohttp-cors"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "ba168754266c6c46b2136207415a5b3a879c957e53e924cab1e64267849ceb90"
+content-hash = "6697909ae6471ce4a4329e6c04eaf64acdf3f2fe0271f5f807c62e979c986206"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -10,6 +10,7 @@ scripts = {ollama = "ollama.cmd.cli:main"}
 [tool.poetry.dependencies]
 python = "^3.11"
 llama-cpp-python = "^0.1.66"
+jinja2 = "^3.1.2"

 aiohttp = {version = "^3.8.4", optional = true}
 aiohttp-cors = {version = "^0.7.0", optional = true}
--- a/requirements.txt
+++ b/requirements.txt
@ -1,337 +1,62 @@
-aiohttp-cors==0.7.0 ; python_version >= "3.11" and python_version < "4.0" \
-    --hash=sha256:0451ba59fdf6909d0e2cd21e4c0a43752bc0703d33fc78ae94d9d9321710193e \
-    --hash=sha256:4d39c6d7100fd9764ed1caf8cebf0eb01bf5e3f24e2e073fda6234bc48b19f5d
-aiohttp==3.8.4 ; python_version >= "3.11" and python_version < "4.0" \
-    --hash=sha256:03543dcf98a6619254b409be2d22b51f21ec66272be4ebda7b04e6412e4b2e14 \
-    --hash=sha256:03baa76b730e4e15a45f81dfe29a8d910314143414e528737f8589ec60cf7391 \
-    --hash=sha256:0a63f03189a6fa7c900226e3ef5ba4d3bd047e18f445e69adbd65af433add5a2 \
-    --hash=sha256:10c8cefcff98fd9168cdd86c4da8b84baaa90bf2da2269c6161984e6737bf23e \
-    --hash=sha256:147ae376f14b55f4f3c2b118b95be50a369b89b38a971e80a17c3fd623f280c9 \
-    --hash=sha256:176a64b24c0935869d5bbc4c96e82f89f643bcdf08ec947701b9dbb3c956b7dd \
-    --hash=sha256:17b79c2963db82086229012cff93ea55196ed31f6493bb1ccd2c62f1724324e4 \
-    --hash=sha256:1a45865451439eb320784918617ba54b7a377e3501fb70402ab84d38c2cd891b \
-    --hash=sha256:1b3ea7edd2d24538959c1c1abf97c744d879d4e541d38305f9bd7d9b10c9ec41 \
-    --hash=sha256:22f6eab15b6db242499a16de87939a342f5a950ad0abaf1532038e2ce7d31567 \
-    --hash=sha256:3032dcb1c35bc330134a5b8a5d4f68c1a87252dfc6e1262c65a7e30e62298275 \
-    --hash=sha256:33587f26dcee66efb2fff3c177547bd0449ab7edf1b73a7f5dea1e38609a0c54 \
-    --hash=sha256:34ce9f93a4a68d1272d26030655dd1b58ff727b3ed2a33d80ec433561b03d67a \
-    --hash=sha256:3a80464982d41b1fbfe3154e440ba4904b71c1a53e9cd584098cd41efdb188ef \
-    --hash=sha256:3b90467ebc3d9fa5b0f9b6489dfb2c304a1db7b9946fa92aa76a831b9d587e99 \
-    --hash=sha256:3d89efa095ca7d442a6d0cbc755f9e08190ba40069b235c9886a8763b03785da \
-    --hash=sha256:3d8ef1a630519a26d6760bc695842579cb09e373c5f227a21b67dc3eb16cfea4 \
-    --hash=sha256:3f43255086fe25e36fd5ed8f2ee47477408a73ef00e804cb2b5cba4bf2ac7f5e \
-    --hash=sha256:40653609b3bf50611356e6b6554e3a331f6879fa7116f3959b20e3528783e699 \
-    --hash=sha256:41a86a69bb63bb2fc3dc9ad5ea9f10f1c9c8e282b471931be0268ddd09430b04 \
-    --hash=sha256:493f5bc2f8307286b7799c6d899d388bbaa7dfa6c4caf4f97ef7521b9cb13719 \
-    --hash=sha256:4a6cadebe132e90cefa77e45f2d2f1a4b2ce5c6b1bfc1656c1ddafcfe4ba8131 \
-    --hash=sha256:4c745b109057e7e5f1848c689ee4fb3a016c8d4d92da52b312f8a509f83aa05e \
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+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
 llama-cpp-python==0.1.66 ; python_version >= "3.11" and python_version < "4.0" \
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+markupsafe==2.1.3 ; python_version >= "3.11" and python_version < "4.0" \
+    --hash=sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e \
+    --hash=sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e \
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+    --hash=sha256:282c2cb35b5b673bbcadb33a585408104df04f14b2d9b01d4c345a3b92861c2c \
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+    --hash=sha256:68e78619a61ecf91e76aa3e6e8e33fc4894a2bebe93410754bd28fce0a8a4f9f \
+    --hash=sha256:69c0f17e9f5a7afdf2cc9fb2d1ce6aabdb3bafb7f38017c0b77862bcec2bbad8 \
+    --hash=sha256:6b2b56950d93e41f33b4223ead100ea0fe11f8e6ee5f641eb753ce4b77a7042b \
+    --hash=sha256:787003c0ddb00500e49a10f2844fac87aa6ce977b90b0feaaf9de23c22508b24 \
+    --hash=sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea \
+    --hash=sha256:8023faf4e01efadfa183e863fefde0046de576c6f14659e8782065bcece22198 \
+    --hash=sha256:8758846a7e80910096950b67071243da3e5a20ed2546e6392603c096778d48e0 \
+    --hash=sha256:8afafd99945ead6e075b973fefa56379c5b5c53fd8937dad92c662da5d8fd5ee \
+    --hash=sha256:8c41976a29d078bb235fea9b2ecd3da465df42a562910f9022f1a03107bd02be \
+    --hash=sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2 \
+    --hash=sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707 \
+    --hash=sha256:962f82a3086483f5e5f64dbad880d31038b698494799b097bc59c2edf392fce6 \
+    --hash=sha256:9dcdfd0eaf283af041973bff14a2e143b8bd64e069f4c383416ecd79a81aab58 \
+    --hash=sha256:aa7bd130efab1c280bed0f45501b7c8795f9fdbeb02e965371bbef3523627779 \
+    --hash=sha256:ab4a0df41e7c16a1392727727e7998a467472d0ad65f3ad5e6e765015df08636 \
+    --hash=sha256:ad9e82fb8f09ade1c3e1b996a6337afac2b8b9e365f926f5a61aacc71adc5b3c \
+    --hash=sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad \
+    --hash=sha256:b076b6226fb84157e3f7c971a47ff3a679d837cf338547532ab866c57930dbee \
+    --hash=sha256:b7ff0f54cb4ff66dd38bebd335a38e2c22c41a8ee45aa608efc890ac3e3931bc \
+    --hash=sha256:bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 \
+    --hash=sha256:c011a4149cfbcf9f03994ec2edffcb8b1dc2d2aede7ca243746df97a5d41ce48 \
+    --hash=sha256:c9c804664ebe8f83a211cace637506669e7890fec1b4195b505c214e50dd4eb7 \
+    --hash=sha256:ca379055a47383d02a5400cb0d110cef0a776fc644cda797db0c5696cfd7e18e \
+    --hash=sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b \
+    --hash=sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa \
+    --hash=sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5 \
+    --hash=sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e \
+    --hash=sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb \
+    --hash=sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9 \
+    --hash=sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57 \
+    --hash=sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc \
+    --hash=sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2
 numpy==1.25.0 ; python_version >= "3.11" and python_version < "4.0" \
    --hash=sha256:0ac6edfb35d2a99aaf102b509c8e9319c499ebd4978df4971b94419a116d0790 \
    --hash=sha256:26815c6c8498dc49d81faa76d61078c4f9f0859ce7817919021b9eba72b425e3 \
@ -361,78 +86,3 @@ numpy==1.25.0 ; python_version >= "3.11" and python_version < "4.0" \
 typing-extensions==4.6.3 ; python_version >= "3.11" and python_version < "4.0" \
    --hash=sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26 \
    --hash=sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5
-yarl==1.9.2 ; python_version >= "3.11" and python_version < "4.0" \
-    --hash=sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571 \
-    --hash=sha256:066c163aec9d3d073dc9ffe5dd3ad05069bcb03fcaab8d221290ba99f9f69ee3 \
-    --hash=sha256:13414591ff516e04fcdee8dc051c13fd3db13b673c7a4cb1350e6b2ad9639ad3 \
-    --hash=sha256:149ddea5abf329752ea5051b61bd6c1d979e13fbf122d3a1f9f0c8be6cb6f63c \
-    --hash=sha256:159d81f22d7a43e6eabc36d7194cb53f2f15f498dbbfa8edc8a3239350f59fe7 \
-    --hash=sha256:1b1bba902cba32cdec51fca038fd53f8beee88b77efc373968d1ed021024cc04 \
-    --hash=sha256:22a94666751778629f1ec4280b08eb11815783c63f52092a5953faf73be24191 \
-    --hash=sha256:2a96c19c52ff442a808c105901d0bdfd2e28575b3d5f82e2f5fd67e20dc5f4ea \
-    --hash=sha256:2b0738fb871812722a0ac2154be1f049c6223b9f6f22eec352996b69775b36d4 \
-    --hash=sha256:2c315df3293cd521033533d242d15eab26583360b58f7ee5d9565f15fee1bef4 \
-    --hash=sha256:32f1d071b3f362c80f1a7d322bfd7b2d11e33d2adf395cc1dd4df36c9c243095 \
-    --hash=sha256:3458a24e4ea3fd8930e934c129b676c27452e4ebda80fbe47b56d8c6c7a63a9e \
-    --hash=sha256:38a3928ae37558bc1b559f67410df446d1fbfa87318b124bf5032c31e3447b74 \
-    --hash=sha256:3da8a678ca8b96c8606bbb8bfacd99a12ad5dd288bc6f7979baddd62f71c63ef \
-    --hash=sha256:494053246b119b041960ddcd20fd76224149cfea8ed8777b687358727911dd33 \
-    --hash=sha256:50f33040f3836e912ed16d212f6cc1efb3231a8a60526a407aeb66c1c1956dde \
-    --hash=sha256:52a25809fcbecfc63ac9ba0c0fb586f90837f5425edfd1ec9f3372b119585e45 \
-    --hash=sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf \
-    --hash=sha256:5415d5a4b080dc9612b1b63cba008db84e908b95848369aa1da3686ae27b6d2b \
-    --hash=sha256:5610f80cf43b6202e2c33ba3ec2ee0a2884f8f423c8f4f62906731d876ef4fac \
-    --hash=sha256:566185e8ebc0898b11f8026447eacd02e46226716229cea8db37496c8cdd26e0 \
-    --hash=sha256:56ff08ab5df8429901ebdc5d15941b59f6253393cb5da07b4170beefcf1b2528 \
-    --hash=sha256:59723a029760079b7d991a401386390c4be5bfec1e7dd83e25a6a0881859e716 \
-    --hash=sha256:5fcd436ea16fee7d4207c045b1e340020e58a2597301cfbcfdbe5abd2356c2fb \
-    --hash=sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18 \
-    --hash=sha256:63c48f6cef34e6319a74c727376e95626f84ea091f92c0250a98e53e62c77c72 \
-    --hash=sha256:646d663eb2232d7909e6601f1a9107e66f9791f290a1b3dc7057818fe44fc2b6 \
-    --hash=sha256:662e6016409828ee910f5d9602a2729a8a57d74b163c89a837de3fea050c7582 \
-    --hash=sha256:674ca19cbee4a82c9f54e0d1eee28116e63bc6fd1e96c43031d11cbab8b2afd5 \
-    --hash=sha256:6a5883464143ab3ae9ba68daae8e7c5c95b969462bbe42e2464d60e7e2698368 \
-    --hash=sha256:6e7221580dc1db478464cfeef9b03b95c5852cc22894e418562997df0d074ccc \
-    --hash=sha256:75df5ef94c3fdc393c6b19d80e6ef1ecc9ae2f4263c09cacb178d871c02a5ba9 \
-    --hash=sha256:783185c75c12a017cc345015ea359cc801c3b29a2966c2655cd12b233bf5a2be \
-    --hash=sha256:822b30a0f22e588b32d3120f6d41e4ed021806418b4c9f0bc3048b8c8cb3f92a \
-    --hash=sha256:8288d7cd28f8119b07dd49b7230d6b4562f9b61ee9a4ab02221060d21136be80 \
-    --hash=sha256:82aa6264b36c50acfb2424ad5ca537a2060ab6de158a5bd2a72a032cc75b9eb8 \
-    --hash=sha256:832b7e711027c114d79dffb92576acd1bd2decc467dec60e1cac96912602d0e6 \
-    --hash=sha256:838162460b3a08987546e881a2bfa573960bb559dfa739e7800ceeec92e64417 \
-    --hash=sha256:83fcc480d7549ccebe9415d96d9263e2d4226798c37ebd18c930fce43dfb9574 \
-    --hash=sha256:84e0b1599334b1e1478db01b756e55937d4614f8654311eb26012091be109d59 \
-    --hash=sha256:891c0e3ec5ec881541f6c5113d8df0315ce5440e244a716b95f2525b7b9f3608 \
-    --hash=sha256:8c2ad583743d16ddbdf6bb14b5cd76bf43b0d0006e918809d5d4ddf7bde8dd82 \
-    --hash=sha256:8c56986609b057b4839968ba901944af91b8e92f1725d1a2d77cbac6972b9ed1 \
-    --hash=sha256:8ea48e0a2f931064469bdabca50c2f578b565fc446f302a79ba6cc0ee7f384d3 \
-    --hash=sha256:8ec53a0ea2a80c5cd1ab397925f94bff59222aa3cf9c6da938ce05c9ec20428d \
-    --hash=sha256:95d2ecefbcf4e744ea952d073c6922e72ee650ffc79028eb1e320e732898d7e8 \
-    --hash=sha256:9b3152f2f5677b997ae6c804b73da05a39daa6a9e85a512e0e6823d81cdad7cc \
-    --hash=sha256:9bf345c3a4f5ba7f766430f97f9cc1320786f19584acc7086491f45524a551ac \
-    --hash=sha256:a60347f234c2212a9f0361955007fcf4033a75bf600a33c88a0a8e91af77c0e8 \
-    --hash=sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955 \
-    --hash=sha256:a83503934c6273806aed765035716216cc9ab4e0364f7f066227e1aaea90b8d0 \
-    --hash=sha256:ac9bb4c5ce3975aeac288cfcb5061ce60e0d14d92209e780c93954076c7c4367 \
-    --hash=sha256:aff634b15beff8902d1f918012fc2a42e0dbae6f469fce134c8a0dc51ca423bb \
-    --hash=sha256:b03917871bf859a81ccb180c9a2e6c1e04d2f6a51d953e6a5cdd70c93d4e5a2a \
-    --hash=sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623 \
-    --hash=sha256:b25322201585c69abc7b0e89e72790469f7dad90d26754717f3310bfe30331c2 \
-    --hash=sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6 \
-    --hash=sha256:b8cc1863402472f16c600e3e93d542b7e7542a540f95c30afd472e8e549fc3f7 \
-    --hash=sha256:b9a4e67ad7b646cd6f0938c7ebfd60e481b7410f574c560e455e938d2da8e0f4 \
-    --hash=sha256:be6b3fdec5c62f2a67cb3f8c6dbf56bbf3f61c0f046f84645cd1ca73532ea051 \
-    --hash=sha256:bf74d08542c3a9ea97bb8f343d4fcbd4d8f91bba5ec9d5d7f792dbe727f88938 \
-    --hash=sha256:c027a6e96ef77d401d8d5a5c8d6bc478e8042f1e448272e8d9752cb0aff8b5c8 \
-    --hash=sha256:c0c77533b5ed4bcc38e943178ccae29b9bcf48ffd1063f5821192f23a1bd27b9 \
-    --hash=sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3 \
-    --hash=sha256:c3a53ba34a636a256d767c086ceb111358876e1fb6b50dfc4d3f4951d40133d5 \
-    --hash=sha256:d4e2c6d555e77b37288eaf45b8f60f0737c9efa3452c6c44626a5455aeb250b9 \
-    --hash=sha256:de119f56f3c5f0e2fb4dee508531a32b069a5f2c6e827b272d1e0ff5ac040333 \
-    --hash=sha256:e65610c5792870d45d7b68c677681376fcf9cc1c289f23e8e8b39c1485384185 \
-    --hash=sha256:e9fdc7ac0d42bc3ea78818557fab03af6181e076a2944f43c38684b4b6bed8e3 \
-    --hash=sha256:ee4afac41415d52d53a9833ebae7e32b344be72835bbb589018c9e938045a560 \
-    --hash=sha256:f364d3480bffd3aa566e886587eaca7c8c04d74f6e8933f3f2c996b7f09bee1b \
-    --hash=sha256:f3b078dbe227f79be488ffcfc7a9edb3409d018e0952cf13f15fd6512847f3f7 \
-    --hash=sha256:f4e2d08f07a3d7d3e12549052eb5ad3eab1c349c53ac51c209a0e5991bbada78 \
-    --hash=sha256:f7a3d8146575e08c29ed1cd287068e6d02f1c7bdff8970db96683b9591b86ee7
--- a/template.py
+++ b/template.py
@ -1,28 +0,0 @@
-from difflib import SequenceMatcher
-
-model_prompts = {
-    "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",
-    "gpt4": "### Instruction:\n{prompt}\n\n### Response:\n",
-    "hermes": "### Instruction:\n{prompt}\n\n### Response:\n",
-    "oasst": "{prompt}",
-    "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:",
-    "qlora": "### Human: {prompt}\n### Assistant:",
-    "tulu": "\n{prompt}\n\n",
-    "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:",
-    "wizardlm": "{prompt}\n\n### Response:",
-}
-
-
-def template(model, prompt):
-    max_ratio = 0
-    closest_key = ""
-    model_name = model.lower()
-    # Find the specialized prompt with the closest name match
-    for key in model_prompts.keys():
-        ratio = SequenceMatcher(None, model_name, key).ratio()
-        if ratio > max_ratio:
-            max_ratio = ratio
-            closest_key = key
-    # Return the value of the closest match
-    p = model_prompts.get(closest_key)  # TODO: provide a better default template
-    return p.format(prompt=prompt)