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

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

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

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

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
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• gradient_clipping: 0.3
• total_train_batch_size: 128
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 1
• mixed_precision_training: bf16

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Evaluation

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Summary

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Environmental Impact

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

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