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Model: theprint/Hemispheres-v0.2-Llama3.2-3B-Final Source: Original Platform
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Hemispheres-v0.2-Llama3.2-3B-Final.F16.gguf
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Hemispheres-v0.2-Llama3.2-3B-Final.Q5_K_M.gguf
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Hemispheres-v0.2-Llama3.2-3B-Final.Q8_0.gguf
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Modelfile
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Modelfile
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FROM /content/theprint/Hemispheres-v0.2-Llama3.2-3B-Final/unsloth.F16.gguf
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TEMPLATE """{{ if .Messages }}
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{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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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.
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{{- end }}
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{{- end }}<|eot_id|>
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 }}
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{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
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{{- if and $.Tools $last }}
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Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
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Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
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{{ $.Tools }}
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{{- end }}
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
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{{- if .ToolCalls }}
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{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
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{{- else }}
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{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
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{{- end }}
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{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|start_header_id|>system<|end_header_id|>
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{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
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{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
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PARAMETER stop "<|start_header_id|>"
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PARAMETER stop "<|end_header_id|>"
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PARAMETER stop "<|eot_id|>"
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PARAMETER stop "<|eom_id|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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README.md
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README.md
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---
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base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- en
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datasets:
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- theprint/Hemispheres-v0.2-Final-gpt
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---
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# Uploaded model
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- **Developed by:** theprint
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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37
adapter_config.json
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/llama-3.2-3b-instruct-bnb-4bit",
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_bias": false,
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"lora_dropout": 0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"down_proj",
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"up_proj",
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"k_proj",
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"o_proj",
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"gate_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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3
adapter_model.safetensors
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e8c9eacb231aa4baa58c7833f5a833cffdf9e498153a6c842c665934c43848bd
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size 97307544
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2070
config.json
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2070
config.json
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