🔥 Unleash the Magic: A Deep Dive into Open-Source Language Models!

• The Quest for the Ideal LLM: Join us as we navigate the vast universe of Large Language Models (LLMs) to find the perfect match for your AI journey.

• Mixtral 8x7B Instruct: Discover the versatile champion armed with 46.7 billion parameters, delivering exceptional performance across various tasks.

• Mistral 7B Instruct: Uncover the hidden gem offering cost-effective efficiency and commendable performance, ideal for experimentation and production.

• Zephyr 7B: Meet the chosen one in alignment and utility, striking the perfect balance between helpfulness and integrity.

• Code Llama: Calling all developers! Explore the master of the coding realm, offering specialized variants and superior performance.

• Llama 2: Embark on a journey of customization and flexibility with Llama 2, the canvas for your AI masterpiece.

• Who: AI enthusiasts, developers, business owners, and marketers eager to harness the power of LLMs.

• What: A deep dive into the world of open-source LLMs, exploring their strengths, weaknesses, and suitability for diverse applications.

• When: Now! Dive into this comprehensive guide to embark on an epic quest for the perfect LLM companion.

• Where: The vast expanse of the internet, where AI innovation knows no bounds.

• Why: To empower readers with the knowledge needed to navigate the dynamic landscape of LLMs and unlock new avenues of innovation.

Embark on a journey of discovery as we navigate through the vast universe of LLMs. Whether you're a seasoned AI explorer or a brave newbie venturing into uncharted territory, finding the right LLM is crucial for success.

Behold, the mighty Mixtral 8x7B Instruct, a versatile warrior armed with the power of 46.7 billion parameters! From slaying code dragons to mastering chat conversations, this champion delivers top-notch performance with a dash of flair.

• Exceptional Output Quality: Prepare to be amazed by Mixtral 8x7B's prowess in every task it tackles!

• Versatile Use Cases: Chatbots, code generation, you name it – Mixtral 8x7B conquers them all with ease!

• Cost-Effective Operation: Save your gold coins for more important quests with Mixtral 8x7B's efficient inference.

• Licensing Freedom: Unleash your creativity without limits, thanks to Mixtral 8x7B's Apache 2.0 license!

But beware, adventurer! Even the mightiest warriors have their weaknesses, so tread carefully in the realm of batching model requests and alignment nuances.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

Example usage - OpenAI Chat Completions Token Streaming Example

This code example shows how to invoke the model with the OpenAI Chat Completions API. The model has three main inputs:

• messages: This is a list of JSON objects. Each of those JSON objects should have a key called role which can have the value of either user or assistant. The JSON object should also have content which is the text passed to the large language model.

• stream: Setting this to True allows you to stream the tokens as they get generated.

• max_tokens: Allows you to control the length of the output sequence.

from openai import OpenAI
import os

# Replace the empty string with your model id below
model_id = ""

client = OpenAI(
   api_key=os.environ["BASETEN_API_KEY"],
   base_url=f"https://bridge.baseten.co/{model_id}/v1"
)

# Call model endpoint
res = client.chat.completions.create(
 model="mistral-7b",
 messages=[
   {"role": "user", "content": "What is a mistral?"},
   {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
   {"role": "user", "content": "How does the mistral wind form?"}
 ],
 temperature=0.9,
 max_tokens=512,
 stream=True
)

# Print the generated tokens as they get streamed
for chunk in res:
    print(chunk.choices[0].delta.content)

1[
2    "A",
3    "mistral",
4    "is",
5    "a",
6    "type",
7    "...."
8]

The code example below shows how to use the same OpenAI Chat Completions API but without token streaming. To do this simply remove stream from the API call.

The output will be the entire generated text produced by the model.

from openai import OpenAI
import os

# Replace the empty string with your model id below
model_id = ""

client = OpenAI(
   api_key=os.environ["BASETEN_API_KEY"],
   base_url=f"https://bridge.baseten.co/{model_id}/v1"
)

# Call model endpoint
res = client.chat.completions.create(
 model="mistral-7b",
 messages=[
   {"role": "user", "content": "What is a mistral?"},
   {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
   {"role": "user", "content": "How does the mistral wind form?"}
 ],
 temperature=0.9,
 max_tokens=512
)

# Print the output of the model
print(res.choices[0].message.content)

1{
2    "output": "<s> [INST] What is a mistral? [/INST] A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour. </s><s> [INST] How does the mistral wind form? [/INST] The mistral wind forms as a result of a pressure gradient between a high-pressure system over the Atlantic Ocean and a low-pressure system over the Mediterranean Sea."
3}

Using the OpenAI Chat Completions API is optional. You can also make a REST API call using the requests library. To invoke the model using this method you need to same three inputs messages , stream, and max_new_tokens.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
    {"role": "user", "content": "What is a mistral?"},
    {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
    {"role": "user", "content": "How does the mistral wind form?"},
]
data = {
    "messages": messages,
    "stream": True,
    "max_new_tokens": 512,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data,
    stream=True
)

# Print the generated tokens as they get streamed
for content in res.iter_content():
    print(content.decode("utf-8"), end="", flush=True)

1[
2    "A",
3    "mistral",
4    "is",
5    "a",
6    "type",
7    "...."
8]

If you don't want to stream the tokens simply set the stream parameter to False.

The output is the entire text generated by the model.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
    {"role": "user", "content": "What is a mistral?"},
    {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
    {"role": "user", "content": "How does the mistral wind form?"},
]
data = {
    "messages": messages,
    "stream": False,
    "max_new_tokens": 512,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data
)

# Print the output of the model
print(res.json())

1{
2    "output": "<s> [INST] What is a mistral? [/INST] A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour. </s><s> [INST] How does the mistral wind form? [/INST] The mistral wind forms as a result of a pressure gradient between a high-pressure system over the Atlantic Ocean and a low-pressure system over the Mediterranean Sea."
3}

For those seeking the perfect blend of performance and thriftiness, Mistral 7B Instruct shines like a precious gem. With its smaller parameter count and commendable efficiency, this underdog proves that size isn't everything.

• Cost-Effective Performance: Experience top-tier performance without breaking the bank!

• Permissive Licensing: Embrace the freedom to explore new horizons with Mistral 7B's Apache 2.0 license!

• Suitable for Various Tasks: From short chats to complex algorithms, Mistral 7B rises to the challenge!

But beware, brave souls, for Mistral 7B may falter in the face of extended conversations and intricate alignments.

Example usage

OpenAI Chat Completions Token Streaming Example

This code example shows how to invoke the model with the OpenAI Chat Completions API. The model has three main inputs:

• messages: This is a list of JSON objects. Each of those JSON objects should have a key called role which can have the value of either user or assistant. The JSON object should also have content which is the text passed to the large language model.

• stream: Setting this to True allows you to stream the tokens as they get generated.

• max_tokens: Allows you to control the length of the output sequence.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

from openai import OpenAI
import os

# Replace the empty string with your model id below
model_id = ""

client = OpenAI(
   api_key=os.environ["BASETEN_API_KEY"],
   base_url=f"https://bridge.baseten.co/{model_id}/v1"
)

# Call model endpoint
res = client.chat.completions.create(
 model="mistral-7b",
 messages=[
   {"role": "user", "content": "What is a mistral?"},
   {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
   {"role": "user", "content": "How does the mistral wind form?"}
 ],
 temperature=0.5,
 max_tokens=50,
 top_p=0.95,
 stream=True
)

# Print the generated tokens as they get streamed
for chunk in res:
    print(chunk.choices[0].delta.content)

1[
2    "A",
3    "mistral",
4    "is",
5    "a",
6    "type",
7    "...."
8]

The code example below shows how to use the same OpenAI Chat Completions API but without token streaming. To do this simply remove stream from the API call.

The output will be the entire generated text produced by the model.

from openai import OpenAI
import os

# Replace the empty string with your model id below
model_id = ""

client = OpenAI(
   api_key=os.environ["BASETEN_API_KEY"],
   base_url=f"https://bridge.baseten.co/{model_id}/v1"
)

# Call model endpoint
res = client.chat.completions.create(
 model="mistral-7b",
 messages=[
   {"role": "user", "content": "What is a mistral?"},
   {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
   {"role": "user", "content": "How does the mistral wind form?"}
 ],
 temperature=0.5,
 max_tokens=50,
 top_p=0.95
)

# Print the output of the model
print(res.choices[0].message.content)

1{
2    "output": "[INST] What is a mistral? [/INST]A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour.  [INST] How does the mistral wind form? [/INST]The mistral wind forms as a result of the movement of cold air from the high mountains of the Swiss Alps towards the sea. The cold air collides with the warmer air over the Mediterranean Sea, causing the cold air to rise rapidly and creating a cyclonic circulation. As the warm air rises, the cold air flows into the valley, creating a strong, steady wind known as the mistral.\n\nThe mistral is typically strongest during the winter months when the air is cold."
3}

Using the OpenAI Chat Completions API is optional. You can also make a REST API call using the requests library. To invoke the model using this method you need to same three inputs messages , stream, and max_new_tokens.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
    {"role": "user", "content": "What is a mistral?"},
    {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
    {"role": "user", "content": "How does the mistral wind form?"},
]
data = {
    "messages": messages,
    "stream": True,
    "max_new_tokens": 512,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data,
    stream=True
)

# Print the generated tokens as they get streamed
for content in res.iter_content():
    print(content.decode("utf-8"), end="", flush=True)

1[
2    "A",
3    "mistral",
4    "is",
5    "a",
6    "type",
7    "...."
8]

If you don't want to stream the tokens simply set the stream parameter to False.

The output is the entire text generated by the model.

import requests

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
    {"role": "user", "content": "What is a mistral?"},
    {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
    {"role": "user", "content": "How does the mistral wind form?"},
]
data = {
    "messages": messages,
    "stream": False,
    "max_new_tokens": 512,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data
)

# Print the output of the model
print(res.json())

1{
2    "output": "[INST] What is a mistral? [/INST]A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour.  [INST] How does the mistral wind form? [/INST]The mistral wind forms as a result of the movement of cold air from the high mountains of the Swiss Alps towards the sea. The cold air collides with the warmer air over the Mediterranean Sea, causing the cold air to rise rapidly and creating a cyclonic circulation. As the warm air rises, the cold air flows into the valley, creating a strong, steady wind known as the mistral.\n\nThe mistral is typically strongest during the winter months when the air is cold."
3}

In the realm of alignment and utility, Zephyr 7B emerges as the chosen one. Crafted by the skilled hands of Hugging Face's H4 research team, this noble hero strikes the perfect balance between helpfulness and integrity.

Balanced Alignment: Experience the magic of Zephyr 7B's helpful assistant behavior without the risk of generating problematic content!

Compatibility: Forge alliances with ease, as Zephyr 7B inherits Mistral's permissive commercial licensing!

But tread cautiously, for Zephyr 7B's powers may wane in domains beyond its expertise.

Example usage

OpenAI Chat Completions Streaming Example

This code example shows how to invoke the model with the OpenAI Chat Completions API. The model has three main inputs:

• messages: This is a list of JSON objects. Each of those JSON objects should have a key called role which can have the value of either user or assistant. The JSON object should also have content which is the text passed to the large language model.

• stream: Setting this to True allows you to stream the tokens as they get generated.

• max_tokens: Allows you to control the length of the output sequence.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

from openai import OpenAI
import os

# Replace the empty string with your model id below
model_id = ""

client = OpenAI(
   api_key=os.environ["BASETEN_API_KEY"],
   base_url=f"https://bridge.baseten.co/{model_id}/v1"
)

# Call model endpoint
response = client.chat.completions.create(
 model="zephyr-7b-alpha",
 messages=[
   {"role": "user", "content": "What is a zephyr?"}
 ],
 temperature=0.9,
 max_tokens=128,
 stream=True
)

# Print the generated tokens as they get streamed
for chunk in response:
    print(chunk.choices[0].delta.content)

1[
2    "A",
3    "zephyr",
4    "is",
5    "a",
6    "gentle",
7    "...."
8]

Using the OpenAI Chat Completions API is optional. You can also make a REST API call using the requests library. To invoke the model using this method you need to same three inputs messages , stream, and max_new_tokens.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
  {"role": "user", "content": "What is a zephyr?"}
]

data = {
    "messages": messages,
    "stream": True,
    "max_new_tokens": 128,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data,
    stream=True
)

# Print the generated tokens as they get streamed
for content in res.iter_content():
    print(content.decode("utf-8"), end="", flush=True

1[
2    "A",
3    "zephyr",
4    "is",
5    "a",
6    "gentle",
7    "...."
8]

If you don't want to stream the tokens simply set the stream parameter to False.

The output is the entire text generated by the model.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

messages = [
  {"role": "user", "content": "What is a zephyr?"}
]

data = {
    "messages": messages,
    "stream": False,
    "max_new_tokens": 128,
    "temperature": 0.9
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data
)

# Print the output of the model
print(res.json())

1{
2    "output": "<|assistant|>\n A zephyr is a gentle, light breeze, especially one blowing from the west in ancient Greek mythology. The word is derived from the Greek word ζέφυρος (zéphyros) which is named after the Greek god of the west wind, Zephyrus. In modern usage, zephyr refers to a light and soft wind, often used to describe winds with speeds under 10 miles per hour (16 kilometers per hour)."
3}

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• Specialized Variants: From 7B to 70B parameters, Code Llama caters to every coding need!

• Superior Performance: Say goodbye to mediocre code generation with Code Llama's exceptional prowess!

But beware, aspiring coders, for Code Llama's powers come with variant-specific capabilities and licensing nuances.

Example usage

This code example shows how to invoke the model using the requests library in Python. The model has a couple of key inputs:

• prompt: The input text sent to the model.

• max_new_tokens: Allows you to control the length of the output sequence.

The output of the model is a JSON object which has a key called output that contains the generated text.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

data = {
    "prompt": "Write some code in python that calculates the meaning of life",
    "max_new_tokens": 512
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data
)

# Print the output of the model
print(res.json())

1{
2    "output": "<summary>Answer</summary>\n\n\t\t```python\n\t\t\t\tdef calculate_meaning_of_life():\n    \t\t\treturn 42\n\t\t```\n"
3}

Flexibility is the name of the game with Llama 2, the canvas for creativity. Whether you're fine-tuning projects or exploring new frontiers, Llama 2 offers a robust foundation for your AI endeavors.

• Size Variety: Choose from 7B, 13B, and 70B sizes to strike the perfect balance between cost and performance!

• Customization Potential: Let your imagination run wild with Llama 2's ample scope for fine-tuning and experimentation!

But beware, bold adventurers, for navigating alignment nuances may prove challenging on your quest with Llama 2.

Example usage

Streaming Token Example

This code example shows how to stream the output tokens as they get generated using Python. The model has three main inputs:

• prompt: The input text sent to the model.

• stream: Setting this to True allows you to stream the tokens as they get generated.

• max_length: Allows you to control the length of the output sequence.

Because this code example streams the tokens as they get generated, it does not produce a JSON output.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

data = {
    "prompt": "What is the difference between a llama and an alpaca?",
    "stream": True,
    "max_length": 512
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data,
    stream=True
)

# Print the generated tokens as they get streamed
for content in res.iter_content():
    print(content.decode("utf-8"), end="", flush=True)

1[
2    "llamas",
3    "and",
4    "alpacas",
5    "are",
6    "..."
7]

If you don't want to stream the tokens simply set the stream parameter to False.

The output is a list containing the generated text.

import requests
import os

# Replace the empty string with your model id below
model_id = ""
baseten_api_key = os.environ["BASETEN_API_KEY"]

data = {
    "prompt": "What is the difference between a llama and an alpaca?",
    "stream": False,
    "max_length": 512
}

# Call model endpoint
res = requests.post(
    f"https://model-{model_id}.api.baseten.co/production/predict",
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data
)

# Print the output of the model
print(res.json())

1[
2    "Great question! Llamas and alpacas are both members of the camelid family, but they are different species with some distinct characteristics. Here are some key differences:\n\n1. Size: Llamas are generally larger than alpacas. Adult llamas can weigh between 280-450 pounds (127-204 kg), while adult alpacas typically weigh between 100-200 pounds (45-91 kg).\n2. Coat: Both llamas and alpacas have soft, fleecy coats, but llamas have a longer coat that can be up to 6 inches (15 cm) long, while alpacas have a shorter coat that is usually around 3 inches (7.6 cm) long.\n3. Ears: Llamas have banana-shaped ears, while alpacas have smaller, more rounded ears.\n4. Tail: Llamas have a long, bushy tail, while alpacas have a shorter, more slender tail.\n5. Habitat: Llamas originated in South America, specifically in the Andean region, while alpacas are native to the Andes mountains in Peru.\n6. Temperament: Llamas are known for their independent nature and can be more challenging to train than alpacas, which are generally easier to handle and train.\n7. Purpose: While both llamas and alpacas are raised for their fiber, llamas are often used as pack animals due to their strength and endurance, while alpacas"
3]

In the ever-expanding universe of open-source LLMs, the quest for the perfect companion is an adventure worth undertaking. Whether you choose Mixtral 8x7B for its versatility, Mistral 7B for its efficiency, Zephyr 7B for its alignment, Code Llama for its coding prowess, or Llama 2 for its flexibility, the path to AI greatness begins with you. So gather your courage, sharpen your wits, and embark on this epic journey with the confidence that your perfect LLM awaits!

• Dive into the world of open-source LLMs and explore their capabilities firsthand.

• Evaluate your specific needs and choose the LLM that best aligns with your objectives.

• Experiment with different models to unlock new possibilities and drive innovation in your projects.

• Stay informed about advancements in the field of AI and continue to evolve your understanding and utilization of LLMs.

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