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metadata
language:
  - en
license: apache-2.0
base_model: Locutusque/TinyMistral-248M
datasets:
  - HuggingFaceH4/ultrachat_200k
  - Felladrin/ChatML-ultrachat_200k
  - Open-Orca/OpenOrca
  - Felladrin/ChatML-OpenOrca
  - hkust-nlp/deita-10k-v0
  - Felladrin/ChatML-deita-10k-v0
  - LDJnr/Capybara
  - Felladrin/ChatML-Capybara
  - databricks/databricks-dolly-15k
  - Felladrin/ChatML-databricks-dolly-15k
  - euclaise/reddit-instruct-curated
  - Felladrin/ChatML-reddit-instruct-curated
  - CohereForAI/aya_dataset
  - Felladrin/ChatML-aya_dataset
pipeline_tag: text-generation
widget:
  - 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?
  - messages:
      - role: user
        content: Heya!
      - role: assistant
        content: Hi! How may I help you?
      - role: user
        content: >-
          I'm 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: system
        content: >-
          You are a very creative assistant. User will give you a task, which
          you should complete with all your knowledge.
      - role: user
        content: >-
          Hello! Can you please elaborate a background story of an RPG game
          about wizards and dragons in a sci-fi world?
inference:
  parameters:
    max_new_tokens: 250
    penalty_alpha: 0.5
    top_k: 5
model-index:
  - name: TinyMistral-248M-Chat-v2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 23.29
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 27.39
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 23.52
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 41.32
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 49.01
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
          name: Open LLM Leaderboard

Locutusque's TinyMistral-248M trained on chat datasets

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: 5

Usage Example

from transformers import pipeline

generate = pipeline("text-generation", "Felladrin/TinyMistral-248M-Chat-v2")

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=5,
)

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 32
Max. sequence length 2048
Weight decay 0.01
Warmup ratio 0.1
NEFTune Noise Alpha 5
Optimizer Adam with betas=(0.9,0.999) and epsilon=1e-08
Scheduler cosine
Seed 42

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 27.42
AI2 Reasoning Challenge (25-Shot) 23.29
HellaSwag (10-Shot) 27.39
MMLU (5-Shot) 23.52
TruthfulQA (0-shot) 41.32
Winogrande (5-shot) 49.01
GSM8k (5-shot) 0.00