README.md

metadata

license: apache-2.0
datasets:
  - VMware/open-instruct
base_model: BEE-spoke-data/smol_llama-220M-GQA
inference:
  parameters:
    do_sample: true
    renormalize_logits: true
    temperature: 0.25
    top_p: 0.95
    top_k: 50
    min_new_tokens: 2
    max_new_tokens: 96
    repetition_penalty: 1.04
    no_repeat_ngram_size: 6
    epsilon_cutoff: 0.0006
widget:
  - text: >
      Below is an instruction that describes a task, paired with an input that
      provides further context. Write a response that appropriately completes
      the request.  
         
      ### Instruction:  
        
      Write an ode to Chipotle burritos. 
        
      ### Response:  
    example_title: burritos
model-index:
  - name: smol_llama-220M-open_instruct
    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: 25
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
          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: 29.71
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
          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: 26.11
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
          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: 44.06
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
          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: 50.28
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
          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=BEE-spoke-data/smol_llama-220M-open_instruct
          name: Open LLM Leaderboard

BEE-spoke-data/smol_llama-220M-open_instruct

Please note that this is an experiment, and the model has limitations because it is smol.

prompt format is alpaca.

Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes
the request.  

### Instruction:  

How can I increase my meme production/output? Currently, I only create them in ancient babylonian which is time consuming.  

### Response:

This was not trained using a separate 'inputs' field (as VMware/open-instruct doesn't use one).

Example

Output on the text above ^. The inference API is set to sample with low temp so you should see (at least slightly) different generations each time.

Note that the inference API parameters used here are an initial educated guess, and may be updated over time:

inference:
  parameters:
    do_sample: true
    renormalize_logits: true
    temperature: 0.25
    top_p: 0.95
    top_k: 50
    min_new_tokens: 2
    max_new_tokens: 96
    repetition_penalty: 1.04
    no_repeat_ngram_size: 6
    epsilon_cutoff: 0.0006

Feel free to experiment with the parameters using the model in Python and let us know if you have improved results with other params!

Data

This was trained on VMware/open-instruct so do whatever you want, provided it falls under the base apache-2.0 license :)

---

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.19
AI2 Reasoning Challenge (25-Shot)	25.00
HellaSwag (10-Shot)	29.71
MMLU (5-Shot)	26.11
TruthfulQA (0-shot)	44.06
Winogrande (5-shot)	50.28
GSM8k (5-shot)	0.00