metadata

language:
  - en
license: apache-2.0
tags:
  - text-generation
datasets:
  - THUDM/webglm-qa
  - databricks/databricks-dolly-15k
  - cognitivecomputations/wizard_vicuna_70k_unfiltered
  - totally-not-an-llm/EverythingLM-data-V3
  - Amod/mental_health_counseling_conversations
  - sablo/oasst2_curated
  - starfishmedical/webGPT_x_dolly
  - Open-Orca/OpenOrca
  - mlabonne/chatml_dpo_pairs
base_model: JackFram/llama-68m
widget:
  - text: >-
      <|im_start|>system

      You are a knowledgeable assistant. Help the user as much as you
      can.<|im_end|>

      <|im_start|>user

      How to become healthier?<|im_end|>

      <|im_start|>assistant
  - text: >-
      <|im_start|>system

      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.<|im_end|>

      <|im_start|>user

      Heya!<|im_end|>

      <|im_start|>assistant

      Hi! How may I help you?<|im_end|>

      <|im_start|>user

      I am interested in developing a career in software engineering. What would
      you recommend me to do?<|im_end|>

      <|im_start|>assistant
  - text: >-
      <|im_start|>system

      You are a helpful assistant who provides concise responses.<|im_end|>

      <|im_start|>user

      Hi!<|im_end|>

      <|im_start|>assistant

      Hello there! How may I help you?<|im_end|>

      <|im_start|>user

      I need to build a simple website. Where should I start learning about web
      development?<|im_end|>

      <|im_start|>assistant
  - text: >-
      <|im_start|>system

      You are a very creative assistant. User will give you a task, which you
      should complete with all your knowledge.<|im_end|>

      <|im_start|>user

      Write the background story of an RPG game about wizards and dragons in a
      sci-fi world.<|im_end|>

      <|im_start|>assistant
inference:
  parameters:
    max_new_tokens: 64
    penalty_alpha: 0.5
    top_k: 4
model-index:
  - name: Llama-68M-Chat-v1
    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/Llama-68M-Chat-v1
          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: 28.27
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
          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: 25.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
          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: 47.27
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
          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: 54.3
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
          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/Llama-68M-Chat-v1
          name: Open LLM Leaderboard

A Llama Chat Model of 68M Parameters

Base model: JackFram/llama-68m
Datasets:
Availability in other ML formats:
- GGUF: afrideva/Llama-68M-Chat-v1-GGUF
- ONNX: Felladrin/onnx-Llama-68M-Chat-v1

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.72
AI2 Reasoning Challenge (25-Shot)	23.29
HellaSwag (10-Shot)	28.27
MMLU (5-Shot)	25.18
TruthfulQA (0-shot)	47.27
Winogrande (5-shot)	54.30
GSM8k (5-shot)	0.00