metadata

language:
  - en
license: apache-2.0
datasets:
  - HuggingFaceH4/ultrachat_200k
  - Felladrin/ChatML-ultrachat_200k
base_model: Locutusque/TinyMistral-248M
pipeline_tag: text-generation
widget:
  - messages:
      - role: system
        content: >-
          You are a career counselor. The user will provide you with an
          individual looking for guidance in their professional life, and your
          task is to assist them in determining what careers they are most
          suited for based on their skills, interests, and experience. You
          should also conduct research into the various options available,
          explain the job market trends in different industries, and advice on
          which qualifications would be beneficial for pursuing particular
          fields.
      - role: user
        content: Heya!
      - role: assistant
        content: Hi! How may I help you?
      - role: user
        content: >-
          I am interested in developing a career in software engineering. What
          would you recommend me to do?
  - messages:
      - role: user
        content: Morning!
      - role: assistant
        content: Good morning! How can I help you today?
      - role: user
        content: Could you give me some tips for becoming a healthier person?
  - messages:
      - role: user
        content: Write the specs of a game about mages in a fantasy world.
  - messages:
      - role: user
        content: Tell me about the pros and cons of social media.
  - messages:
      - role: system
        content: >-
          You are a highly knowledgeable and friendly assistant. Your goal is to
          understand and respond to user inquiries with clarity. Your
          interactions are always respectful, helpful, and focused on delivering
          the most accurate information to the user.
      - role: user
        content: Hey! Got a question for you!
      - role: assistant
        content: Sure! What's it?
      - role: user
        content: What are some potential applications for quantum computing?
inference:
  parameters:
    max_new_tokens: 250
    penalty_alpha: 0.5
    top_k: 4
    repetition_penalty: 1.12

Locutusque's TinyMistral-248M trained on UltraChat dataset

Base model: Locutusque/TinyMistral-248M with two additional special tokens (<|im_start|> and <|im_end|>)
Dataset: [ChatML] HuggingFaceH4/ultrachat_200k
License: Apache License 2.0

Recommended Prompt Format

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant

Recommended Inference Parameters

penalty_alpha: 0.5
top_k: 4
repetition_penalty: 1.12

Usage Example

from transformers import pipeline

generate = pipeline("text-generation", "Felladrin/TinyMistral-248M-Chat-v1")

messages = [
    {
        "role": "system",
        "content": "You are a highly knowledgeable and friendly assistant. Your goal is to understand and respond to user inquiries with clarity. Your interactions are always respectful, helpful, and focused on delivering the most accurate information to the user.",
    },
    {
        "role": "user",
        "content": "Hey! Got a question for you!",
    },
    {
        "role": "assistant",
        "content": "Sure! What's it?",
    },
    {
        "role": "user",
        "content": "What are some potential applications for quantum computing?",
    },
]

prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output = generate(
    prompt,
    max_new_tokens=256,
    penalty_alpha=0.5,
    top_k=4,
    repetition_penalty=1.12,
)

print(output[0]["generated_text"])

How it was trained

This model was trained with SFTTrainer using the following settings:

Hyperparameter	Value
Learning rate	2e-5
Total train batch size	16
Max. sequence length	2048
Weight decay	0
Warmup ratio	0.1
Optimizer	Adam with betas=(0.9,0.999) and epsilon=1e-08
Scheduler	cosine
Seed	42