latest
43GB
Tools
70B
3 Pulls Updated 9 days ago
Updated 10 days ago
10 days ago
86f0a9f99006 · 43GB
model
archllama
·
parameters70.6B
·
quantizationQ4_K_M
43GB
params
{"stop":["<|start_header_id|>","<|end_header_id|>","<|eot_id|>"]}
96B
template
{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal use question.
{{- end }}
{{- end }}<|eot_id|>
{{- 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.
{{ $.Tools }}
{{- end }}
{{ .Content }}<|eot_id|>{{ 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 }}{{ if not $last }}<|eot_id|>{{ end }}
{{- 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 }}
1.4kB
Readme
Forge: A Meta Llama 3.1 Based Model
Overview
Forge is a powerful language model based on the popular Meta Llama 3.1 architecture, specifically the 70B Instruct variant. This model has been fine-tuned and optimized for performance, leveraging the strengths of its base model to deliver accurate and informative responses.
Key Features
- Architecture: Based on the well-established LLaMA (Large Language Model) architecture
- Base Model: Meta Llama 3.1 70B Instruct
- Quantization Version: 2
- File Type: Q4_K_M
Model Specifications
- Attention Mechanism: Utilizes 64 attention heads with a head count of 8 for key and value computations
- Layer Normalization: Employs RMS epsilon of 1e-05 for stability and accuracy
- Block Count: Comprises 80 blocks, allowing for deep contextual understanding
- Context Length: Supports sequences up to 131,072 tokens
- Embedding Length: Embeds input tokens into a 8192-dimensional space
- Feed Forward Length: Expands the embedding length to 28672 dimensions through feed-forward layers
- RoPE (Rotary Positional Encoding): Utilizes 128 dimensions and a frequency base of 500,000 for efficient positional encoding
Vocabulary
- Vocabulary Size: Comprises 128,256 unique tokens