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avanishd/DeepSeek-R1-Distill-Qwen-1.5B-finetuned-smoltalk-everyday-conversations

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Model Card

Model Card for DeepSeek-R1-SmolTalk

This model is a fine-tuned version of `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` on the SmolTalk dataset. It is optimized for small-scale, friendly, and engaging instruction-following dialogue.

Model Details

Model Description

This model builds on DeepSeek's distilled Qwen-1.5B architecture and is trained for conversational tasks using the SmolTalk dataset. The goal is to create a lightweight, instruction-following model suitable for use in chatbots or lightweight assistants with limited hardware resources.

  • —Model type: Instruction-tuned causal decoder (chat)
  • —Language(s): English
  • —License: MIT
  • —Finetuned from model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Uses

Direct Use

This model can be used as a lightweight assistant or chatbot in applications such as:

  • —Embedded conversational interfaces
  • —Educational or toy assistants
  • —Small devices or local applications

Downstream Use

The model can be further fine-tuned or integrated into larger conversational systems, especially where resource efficiency is crucial.

Out-of-Scope Use

  • —Not suitable for tasks requiring deep factual accuracy or reasoning
  • —Should not be used for sensitive or high-stakes decision making
  • —Not designed for multilingual use

Bias, Risks, and Limitations

Due to the small model size and dataset limitations:

  • —May produce generic or incorrect outputs
  • —Can reflect biases present in the training dataset
  • —Not guaranteed to be safe for all user demographics or use cases

Recommendations

  • —Use in controlled or sandboxed environments
  • —Consider integrating content moderation or rule-based filtering
  • —Do not deploy in contexts requiring factual correctness or ethical judgment

How to Get Started with the Model

Python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("avanishd/DeepSeek-R1-Distill-Qwen-1.5B-finetuned-smoltalk-everyday-conversations")
tokenizer = AutoTokenizer.from_pretrained("avanishd/DeepSeek-R1-Distill-Qwen-1.5B-finetuned-smoltalk-everyday-conversations")

input_text = "Hi there! What can you do?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

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Used SmolTalk dataset, a dataset of lightweight, instruction-style conversations. The dataset is designed to help models learn concise, friendly, and helpful interactions.

Training Procedure

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Preprocessing [optional]

Used the DeepSeek tokenizer

LoRA Configuration
  • —rank: 6
  • —alpha: 12
  • —dropout: 0.05
  • —bias: none
  • —target: linear
Training Hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 2e-04
  • —trainbatchsize: 2
  • —evalbatchsize: 2
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —gradient_clipping: 0.3
  • —totaltrainbatch_size: 128
  • —optimizer: Use OptimizerNames.ADAMWTORCHFUSED
  • —lrschedulertype: constant
  • —lrschedulerwarmup_ratio: 0.03
  • —num_epochs: 1
  • —mixedprecisiontraining: bf16
Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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