metadata

base_model: BEE-spoke-data/smol_llama-220M-open_instruct
datasets:
  - VMware/open-instruct
inference: false
license: apache-2.0
model_creator: BEE-spoke-data
model_name: smol_llama-220M-open_instruct
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - gguf
  - ggml
  - quantized
  - q2_k
  - q3_k_m
  - q4_k_m
  - q5_k_m
  - q6_k
  - q8_0
widget:
  - example_title: burritos
    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:

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

Quantized GGUF model files for smol_llama-220M-open_instruct from BEE-spoke-data

Name	Quant method	Size
smol_llama-220m-open_instruct.fp16.gguf	fp16	436.50 MB
smol_llama-220m-open_instruct.q2_k.gguf	q2_k	94.43 MB
smol_llama-220m-open_instruct.q3_k_m.gguf	q3_k_m	114.65 MB
smol_llama-220m-open_instruct.q4_k_m.gguf	q4_k_m	137.58 MB
smol_llama-220m-open_instruct.q5_k_m.gguf	q5_k_m	157.91 MB
smol_llama-220m-open_instruct.q6_k.gguf	q6_k	179.52 MB
smol_llama-220m-open_instruct.q8_0.gguf	q8_0	232.28 MB

Original Model Card:

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 :)