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Model: rizkysulaeman/llama-3.2-3B-reasoning-en-ft-v1 Source: Original Platform
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README.md
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README.md
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---
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language: en
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license: apache-2.0
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tags:
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- llama
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- reasoning
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- fine-tuned
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- english
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- 3B
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- conversational
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- gguf
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- 4-bit
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- 5-bit
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- 8-bit
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pipeline_tag: text-generation
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library_name: transformers
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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datasets:
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- openai/gsm8k
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---
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# CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1
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## Model Summary
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**CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1** is a fine-tuned version of Meta's LLaMA 3 3B model, optimized for English-language reasoning tasks. This model has been adapted to enhance performance in logical reasoning, problem-solving, and conversational understanding.
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## Model Details
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- **Developed by**: Mohammad Yani & Rizky Sulaeman, Politeknik Negeri Indramayu
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- **Model type**: Decoder-only transformer (LLaMA 3 architecture)
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- **Parameter count**: 3.21 billion
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- **Quantization formats**: 4-bit (Q4_K_M), 5-bit (Q5_K_M), 8-bit (Q8_0)
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- **Training data**: [Specify datasets or data sources used]
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- **License**: Apache License 2.0
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- **Base model**: [meta-llama/Llama-3-3B](https://huggingface.co/meta-llama/Llama-3-3B)
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## Intended Uses & Limitations
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### Intended Uses
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- **Applications**:
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- Logical reasoning tasks
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- Conversational agents requiring enhanced reasoning capabilities
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- Educational tools focusing on critical thinking
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- **Users**:
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- Researchers in natural language processing
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- Developers building AI-driven applications
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- Educators and students in AI-related fields
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### Limitations
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- The model's performance may degrade on tasks outside its fine-tuned domain.
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- Not suitable for real-time applications without further optimization.
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- May produce incorrect or nonsensical answers; outputs should be verified in critical applications.
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## How to Use
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```python
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# Use a pipeline as a high-level helper
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from transformers import pipeline
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pipe = pipeline("text-generation", model="CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1")
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messages = [
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{"role": "user", "content": "Who are you?"},
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]
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pipe(messages)
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```
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config.json
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config.json
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{
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"model_type": "llama"
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"max_length": 131072,
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"pad_token_id": 0,
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"transformers_version": "4.51.3"
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}
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modelfile_ollama_llama_grpo
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modelfile_ollama_llama_grpo
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FROM C:\Users\Rizky Sulaeman\.ollama\models\blobs\sha256-5e9bedd19374d609f5186b161c7cb32207357185d09059182a464953325c5fe4
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system """You are given a problem.
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Think about the problem and provide your working out.
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Place it between <start_working_out> and <end_working_out>.
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Then, provide your solution between <SOLUTION></SOLUTION>"""
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TEMPLATE """{{- 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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Cutting Knowledge Date: December 2023
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When you receive a tool call response, use the output to format an answer to the orginal user question.
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You are a helpful assistant with tool calling capabilities.
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{{- end }}<|eot_id|>
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{{- end }}
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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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{{ range $.Tools }}
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{{- . }}
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{{ end }}
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Question: {{ .Content }}<|eot_id|>
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{{- else }}
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{{ .Content }}<|eot_id|>
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{{- end }}{{ 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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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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23
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23
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3
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Reference in New Issue
Block a user