Tools 8B

38 Pulls Updated 7 days ago

7 days ago

d4abaa5f66b9 · 8.5GB

model
llama
·
8.03B
·
Q8_0
params
{"stop":["<|start_header_id|>","<|end_header_id|>","<|eot_id|>"]}
template
{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|> {{- if .System }} {{ .System }} {{- end }} {{- if .Tools }} Cutting Knowledge Date: December 2023 When you receive a tool call response, use the output to format an answer to the orginal user question. You are a helpful assistant with tool calling capabilities. {{- end }}<|eot_id|> {{- end }} {{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 }} {{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|> {{- if and $.Tools $last }} Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables. {{ range $.Tools }} {{- . }} {{ end }} Question: {{ .Content }}<|eot_id|> {{- else }} {{ .Content }}<|eot_id|> {{- end }}{{ if $last }}<|start_header_id|>assistant<|end_header_id|> {{ end }} {{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|> {{- if .ToolCalls }} {{ range .ToolCalls }} {"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }} {{- else }} {{ .Content }} {{- end }}{{ if not $last }}<|eot_id|>{{ end }} {{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|> {{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|> {{ end }} {{- end }} {{- end }}

Readme

Llama-3.1-SuperNova-Lite

Overview

Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability.

The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai.

Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.

Evaluations

We will be submitting this model to the OpenLLM Leaderboard for a more conclusive benchmark - but here are our internal benchmarks using the main branch of lm evaluation harness:

Benchmark SuperNova-Lite Llama-3.1-8b-Instruct
IF_Eval 81.1 77.4
MMLU Pro 38.7 37.7
TruthfulQA 64.4 55.0
BBH 51.1 50.6
GPQA 31.2 29.02

https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite