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Llama-3.2-1B-Instruct-bnb-4bit-lima-GGUF

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metadata
base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
library_name: transformers
pipeline_tag: text-generation
tags:
  - gguf
  - fine-tuned
  - lima
language:
  - en
license: apache-2.0

Llama-3.2-1B-Instruct-bnb-4bit-lima - GGUF Format

GGUF format quantizations for llama.cpp/Ollama.

Model Details

Related Models

Prompt Format

This model uses the Llama 3.2 chat template.

Ollama Template Format 

{{ if .Messages }}
{{- 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 original 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 }}
{{- else }}
{{- if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}

Training Details

  • LoRA Rank: 16
  • Training Steps: 129
  • Training Loss: 2.3025
  • Max Seq Length: 4086
  • Training Scope: 1,030 samples (1 epoch(s), full dataset)

For complete training configuration, see the LoRA adapters repository/directory.

Available Quantizations

Quantization File Size Quality
F16 Llama-3.2-1B-Instruct-bnb-4bit-lima-F16.gguf 2.31 GB Full precision (largest)
Q4_K_M Llama-3.2-1B-Instruct-bnb-4bit-lima-Q4_K_M.gguf 0.75 GB Good balance (recommended)
Q6_K Llama-3.2-1B-Instruct-bnb-4bit-lima-Q6_K.gguf 0.95 GB High quality
Q8_0 Llama-3.2-1B-Instruct-bnb-4bit-lima-Q8_0.gguf 1.23 GB Very high quality, near original

Usage: Use the dropdown menu above to select a quantization, then follow HuggingFace's provided instructions.

License

Based on unsloth/Llama-3.2-1B-Instruct-bnb-4bit and trained on GAIR/lima. Please refer to the original model and dataset licenses.

Credits

Trained by: Farhan Syah

Training pipeline:

Base components: